2a:null 2b:["$","title",null,{"children":"Language of Mechanisation | Miguel Vieira | Observable"}] 30:T761,The effects of mechanisation in 19th-century Britain had strong repercussions on various aspects of Late Modern English, particularly on its lexicon. During this particularly dynamic moment in the history of English, its vocabulary underwent rapid changes in both written and spoken sources. Technological advances, social changes, geographical expansion and contact with other languages, are all factors that led to the emergence of new terms and loan formations. New concepts or entities were sometimes expressed with neologisms and loanwords, and sometimes through new meanings of existing words via metaphorical phenomena and semantic innovation. Within _Living with machines_, the work package _Language of Mechanisation_, led by Co-Investigator Barbara McGillivray analyses the changes in the English lexicon related to mechanisation. In 2022 and 2023 we ran a series of tasks in the citizen science platform _Zooniverse_. We asked members of the public to look at articles from 19th century newspapers that mentioned specific types of machine that we thought would yield interesting results. We'd like to thank all our contributors for their classifications, transcriptions, questions and comments! In this notebook, designed and developed by our colleagues from [King's Digital Lab](https://kdl.kcl.ac.uk/projects/living-with-machines/) (Miguel Vieira, Tiffany Ong and Arianna Ciula) in collaboration with Barbara McGillivray, Mia Ridge and Nilo Pedrazzini, we analyse words that changed meaning during the 19th century, examining their connection to the process of mechanisation in British society. Alongside studying temporal changes, we will also explore instances where words varied in meaning across geographical regions or were employed differently in newspapers based on their political alignment. The words we chose to focus on are: _trolley_, _car_, _coach_, and _bike_.31:T7b4,{ const plot = Plot.plot({ color: { legend: true, opacity: 0.9, scheme: "Blues" }, height: 80, marks: [ Plot.rectX( agreement, Plot.binX( { fill: "count" }, { x: { domain: [0, 1.1], thresholds: 20, value: "rate" }, fill: defaultColour, opacity: 0.9, tip: "x" } ) ), Plot.textX([agreementThreshold / 100], { fill: agreementStroke, fontSize: 12, fontWeight: "bold", text: (_) => ` ${agreementThreshold}%`, textAnchor: "start" }), Plot.ruleX([agreementThreshold / 100], { stroke: agreementStroke, strokeWidth: 3 }) ], x: { domain: [0, 105], label: "Agreement rate (%)", percent: true, ticks: [0, 20, 40, 60, 80, 100] }, width }); const line = d3.select(plot).select("g[aria-label='rule']").select("line"); const text = d3.select(plot).select("g[aria-label='text']").select("text"); const initialX = line.attr("x1"); line.call( d3.drag().on("start", dragstarted).on("drag", dragged).on("end", dragended) ); function dragstarted() { d3.select(this).raise(); line.attr("cursor", "grabbing"); } function dragged(event) { const x = event.x; const rate = Math.round(pixelToRate(x)); if (rate <= 0 || rate >= 100) return; set(viewof agreementThreshold, rate); d3.select(this).attr("x1", x); d3.select(this).attr("x2", x); text .text(` ${d3.format(".0%")(rate / 100)}`) .attr("transform", `translate(${x}, 15.25)`); } const pixelToRate = (pixel) => ((pixel - 20) * 65) / (initialX - 20); const set = (input, value) => { input.value = value; input.dispatchEvent(new Event("input", { bubbles: true })); }; function dragended() { line.attr("cursor", "grab"); } return plot; }32:T41c,#### Relative number of annotations The plot below shows the relative frequencies of the annotations for each of the word meanings in each year. Relative frequencies account for the variations in the year values and are calculated by dividing by the total number of annotations for that word in that year. The **curves** in the plots below are created using a *[monotone-x](https://observablehq.com/plot/features/curves#curves)* curve, a curve interpolation method that ensures a smooth and monotonically increasing or decreasing curve when visualising data along the x-axis. The **trend line** (straight) in the plot represents the general direction or pattern of the annotation counts for that word per year. It is generated using linear regression. The **shaded area** in the plot represents the potential variability or imprecision in the data. The range is calculated using [confidence intervals for proportions](https://www.statology.org/confidence-interval-proportion/). The grey lines in the background represent the alternative meaning/s.33:T9e6,{ const isFacet = facetBy !== "year"; const data = annotationsBy[facetBy] .filter((a) => selectedDomains.includes(a.annotation)) .filter((a) => !isFacet || countsByFacet[a[facetBy]] >= facetMinYears) .filter((a) => !isFacet || selectedFacets.includes(a[facetBy])); const x = "year"; const fy = isFacet ? facetBy : null; const xy = { x, y: facetNormaliseToggle ? "norm" : "count", fy }; const y = { axis: !isFacet, grid: false, label: facetNormaliseToggle ? "count (%)" : "count", tickSize: 0, percent: facetNormaliseToggle }; const curve = "monotone-x"; const plot = tooltipPlot().plot({ color: { domain: Object.keys(workflowDomain), range: Object.values(workflowDomain) }, marks: [ isFacet ? [ Plot.axisFy({ label: null, text: null }), Plot.text( data, Plot.selectFirst({ x: yearDomain[0], fy, frameAnchor: "top-left", fontSize: 12, fontWeight: "bold", // lineWidth: 20, text: fy }) ) ] : null, Plot.areaY(data, { filter: (_) => facetConfidenceToggle, x: "year", y1: "lower", y2: "upper", curve, fy, fill: "annotation", fillOpacity: 0.2, mixBlendMode: "multiply" }), Plot.line(data, { ...xy, curve, stroke: "annotation", strokeWidth: 2 }), Plot.linearRegressionY(data, { filter: (_) => facetRegressionToggle, ...xy, stroke: "annotation", strokeWidth: 1 }), Plot.dot(data, { ...xy, fill: "annotation", r: facetNormaliseToggle ? "count" : 3, stroke: "white", title: (d) => title(d) }) ], r: { range: [1, 5] }, x: { axis: isFacet ? "both" : "bottom", domain: yearDomain, grid: false, label: null, labelArrow: null, tickFormat: (d) => formatYear(d), tickSize: 0 }, y, width }); handlePlotClick( plot, data, workflowAnnotations, "annotation", (title, sids) => { mutable selectedTitle = { ...selectedTitle, workflow: title }; mutable selectedSids = { ...selectedSids, workflow: sids }; } ); return plot; }34:T62a,{ const data = annotationsByDecade(cumsumWindow); const y = `cumsumW${cumsumWindow}`; // const shifts = getChangePoints(data, y, shiftThreshold); return Plot.plot({ color: { domain: selectedDomains, range: Object.entries(workflowDomain) .filter(([k, _]) => selectedDomains.includes(k)) .map(([_, v]) => v) }, marginRight: 200, marks: [ Plot.line(data, { x: "decade", y, curve: "monotone-x", stroke: "meaning", strokeWidth: 2 }), // PELT Plot.ruleX( decades.filter((_, i) => peltChangePoints.includes(i)), { strokeWidth: 1.5 // strokeDasharray: [6] } ), // CUSUM // Plot.ruleX(shifts, { // x: "decade" // }), // Plot.dot(shifts, { // x: "decade", // y: "cur", // fill: "meaning", // r: "absChange", // rotate: (d) => (d.change < 0 ? 180 : 0), // stroke: "white", // symbol: "triangle", // tip: "xy" // }), Plot.textX(data, { filter: (d) => d.decade === decades[decades.length - 1], x: "decade", y, dx: 7, fill: "meaning", fontSize: 12, fontWeight: "bold", text: "meaning", textAnchor: "start", lineWidth: 15 }) ], r: { range: [1, 5] }, x: { label: null, labelArrow: null, tickFormat: (d) => formatYear(d), tickSize: 0 }, y: { label: "cusum", tickSize: 0 }, width }); }35:T4c3,{ const data = cumsumData; const domain = [...selectedDomains, "no data"]; const opacity = 1; return Plot.plot({ color: { domain, range: [ ...Object.entries(workflowDomain) .filter(([k, _]) => selectedDomains.includes(k)) .map(([_, v]) => v), "lightgrey" ] }, height: 375, marginLeft: 250, marks: [ Plot.axisY({ fontSize: 12, fontWeight: "bold", label: null, lineWidth: 20, tickSize: 0 }), Plot.dot(data, { x: "decade", y: "meaning", r: 4, fill: "meaning", opacity, strokeWidth: 2, tip: "xy" }), Plot.arrow([meaningsData], { x1: (_, i) => meaningsData[i].decade, y1: (_, i) => meaningsData[i].meaning, x2: (_, i) => meaningsData[meaningsData.length - 1].decade, y2: (_, i) => meaningsData[meaningsData.length - 1].meaning, bend: true, inset: 5, stroke: "#777", strokeWidth: 1.5 }) ], width, x: { domain: decades, label: null, tickFormat: (d) => formatYear(d), tickSize: 0 }, y: { domain } }); 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2e:{"id":"cb2710f3d713df53","slug":"language-of-mechanisation","title":"Language of Mechanisation","thumbnail":"dde0735aa9c799e8c3e4141f79ed5a8e348ba6bd6df25ed48ab505e3747e93e2","access_level":"public","create_time":"$D2023-05-12T13:24:49.301Z","update_time":"$D2025-03-04T14:39:57.443Z","view_time":null,"first_public_time":"$D2023-05-12T13:24:49.301Z","fork":{"id":"63e00b1624bc6a06","slug":"lwm-language-of-accidents","title":"Language of Accidents","owner":{"id":"587db768fa766a06","login":"jmiguelv","name":"Miguel Vieira"}},"stars":2,"starred":false,"owner":{"type":"team","id":"587db768fa766a06","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":"starter_2024"},"version":7202,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null},{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}],"members":[{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null,"role":"editor"},{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null,"role":"editor"},{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null,"role":"editor"},{"type":"individual","id":"13b7cd0ffd6e4a92","login":"mialondon","name":"Mia","avatar_url":"https://avatars-next.observableusercontent.com/718fd54635137cd9a35241d6876cfe3ece957358071d65ea1440f3a156a753a4","tier":null,"role":"editor"},{"type":"individual","id":"8ae44428717bed65","login":"ongtiffany","name":"Tiffany Ong","avatar_url":"https://avatars-next.observableusercontent.com/90bb82ebec84e2a6c0c37b437ab558ae373abc2059b6c334a1b176809bc79a50","tier":null,"role":"editor"}],"comments":[{"id":"989a9f255a9444eb","reply_to_id":null,"content":"Barbara to check wording and integrate as needed","create_time":"$D2023-05-31T08:04:00.047Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"cc030dea0c73a52c","reply_to_id":null,"content":"Barbara to liaise with Mia on how to explain concepts such as active/non active","create_time":"$D2023-05-31T08:04:54.297Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"18f4ec94910f1bfa","reply_to_id":null,"content":"Barbara to check whether to add more contextual information as done for DAVIZCT notebook: https://observablehq.com/@jmiguelv/lwm-language-of-accidents","create_time":"$D2023-05-31T08:05:41.471Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"1c87cd74311f634a","reply_to_id":null,"content":"Barbara to check labels of axis and alert KDL if they need correcting","create_time":"$D2023-05-31T08:08:32.801Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"ada84e6f52b6ea1b","reply_to_id":null,"content":"Barbara to liaise with Mia on how to explain meaning of concepts such as subjects","create_time":"$D2023-05-31T08:08:57.694Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"19c72c745533fb65","reply_to_id":null,"content":"Format of year in table of subjects to be corrected if feasible","create_time":"$D2023-05-31T08:11:38.170Z","update_time":"$D2023-06-01T08:45:21.057Z","resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"390d11e757dbcbed","reply_to_id":"cc030dea0c73a52c","content":"We can put the concepts in a different notebook and then pull in as required.","create_time":"$D2023-05-31T08:59:28.091Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"a70e6cf9bf4fcc89","reply_to_id":"989a9f255a9444eb","content":"I've added a sentence about the Language of mechanisation. \nIn addition, I suggest we explain the terms used, i.e. \"workflows\", \"subjects\"","create_time":"$D2023-05-31T14:53:27.195Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"46a22bdc96c0c184","reply_to_id":null,"content":"Could we have a little introduction explaining what this section is about?","create_time":"$D2023-05-31T14:54:00.112Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"98973439f12c6c89","reply_to_id":"cc030dea0c73a52c","content":"I agree, we should explain what active and non active means","create_time":"$D2023-05-31T14:54:45.282Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"51359e60c48da874","reply_to_id":null,"content":"could we give a definition of annotation agreement?","create_time":"$D2023-05-31T14:56:38.772Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"05ff596c2988454a","reply_to_id":"51359e60c48da874","content":"we also decided to exclude data points with agreement below 0.65 from the subsequent analyses","create_time":"$D2023-05-31T14:57:05.517Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"9bfd84d8889a97e2","reply_to_id":null,"content":"very cool!","create_time":"$D2023-05-31T14:57:49.064Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"2e6e4f78f3f4e115","reply_to_id":null,"content":"I'm not sure I understand what \"normalised by Observable\" means","create_time":"$D2023-05-31T14:58:16.474Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"c31ee71d8de248ff","reply_to_id":"2e6e4f78f3f4e115","content":"I was experimenting with the normalisation models available in the Observable Plot library :)","create_time":"$D2023-05-31T15:08:06.880Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"68e151bc449e0175","reply_to_id":"51359e60c48da874","content":"Miguel added the description; regarding the agreement in our notes I have \"percentage to be determined once other words are analysed so it seems best to wait for all words to be there but in any case threshold added below so left to the user to select it. I think this comment can be resolved now.","create_time":"$D2023-06-01T08:42:14.814Z","update_time":"$D2023-06-01T08:43:49.912Z","resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"447cde073a50d596","reply_to_id":null,"content":"Format of year to be corrected if possible","create_time":"$D2023-06-01T08:42:42.475Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"5dcef772f4e09918","reply_to_id":null,"content":"very clear, thanks!","create_time":"$D2023-06-01T21:19:22.984Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"9167e587ca3cc59c","reply_to_id":null,"content":"\"Normalise...\": maybe we should add something like \"by dividing by the total number of annotations for that word in that year\" at the end?\n\"Display trend line\": add \"patter of the annotation counts for that word per year\"\n\"Display uncertainty...\": representation-->representing. Could you also add how this was calculated (linear regression?)","create_time":"$D2023-06-07T07:24:36.507Z","update_time":"$D2023-06-07T07:25:59.064Z","resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"175656665681fc5d","reply_to_id":"9167e587ca3cc59c","content":"by the way, I really like these plots!","create_time":"$D2023-06-07T07:26:17.600Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"790ddbe4577f6da2","reply_to_id":null,"content":"Are the years being shrunk according to the amount of text in the classification option?","create_time":"$D2023-06-07T16:35:00.698Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"13b7cd0ffd6e4a92","login":"mialondon","name":"Mia","avatar_url":"https://avatars-next.observableusercontent.com/718fd54635137cd9a35241d6876cfe3ece957358071d65ea1440f3a156a753a4","tier":null}},{"id":"1b5363ff0f9465bc","reply_to_id":"790ddbe4577f6da2","content":"No, it has to do with the number of annotations per year. We could pin the min/max year, but it would push a lot of the relevant information to the right and most of it would be blank space.","create_time":"$D2023-06-07T16:44:14.906Z","update_time":"$D2023-06-07T16:44:41.799Z","resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"d68863c3d594c50b","reply_to_id":"19c72c745533fb65","content":"I've selected 'bike or motorbike' as my workflow and 'the image is too hard to read' as my meaning, but the headline above is 'Subjects from 1849, annotated as a horse-drawn private or public carriage for carrying passengers'","create_time":"$D2023-06-07T16:46:16.640Z","update_time":"$D2023-06-07T16:46:34.450Z","resolved":true,"user":{"type":"individual","id":"13b7cd0ffd6e4a92","login":"mialondon","name":"Mia","avatar_url":"https://avatars-next.observableusercontent.com/718fd54635137cd9a35241d6876cfe3ece957358071d65ea1440f3a156a753a4","tier":null}},{"id":"44c368abb5fe2f52","reply_to_id":"790ddbe4577f6da2","content":"I've emailed a screenshot for context. I'd expect the width to remain consistent as it's all options from the same workflow, but some were smaller than others","create_time":"$D2023-06-07T16:47:56.878Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"13b7cd0ffd6e4a92","login":"mialondon","name":"Mia","avatar_url":"https://avatars-next.observableusercontent.com/718fd54635137cd9a35241d6876cfe3ece957358071d65ea1440f3a156a753a4","tier":null}},{"id":"4f161f9e3a837485","reply_to_id":"790ddbe4577f6da2","content":"Followed up on the email in case others want to comment as well.","create_time":"$D2023-06-07T16:59:39.819Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"78a3a04661b80956","reply_to_id":"19c72c745533fb65","content":"I can't reproduce, did you click on a year data point in the image is too hard to read subplot?","create_time":"$D2023-06-07T20:30:30.601Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"4c6909f231c2d805","reply_to_id":"9167e587ca3cc59c","content":"COuld we add an explanation of how the curved lines were created to connect the dots?","create_time":"$D2023-06-14T09:16:33.911Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"6e62c501f64a7657","reply_to_id":null,"content":"could we add links to the sections below about annotators' agreement and Zooniverse data so that the readers are reassured?","create_time":"$D2023-06-14T09:22:46.337Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"ff50e8538678cc19","reply_to_id":"6e62c501f64a7657","content":"and also a link to the glossary","create_time":"$D2023-06-14T09:25:08.893Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"11fcc77597d8a0ff","reply_to_id":null,"content":"is it possible to control the parameters (trend lines, etc.) for each plot separately","create_time":"$D2023-06-14T09:33:58.037Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"2cd535e921a95489","reply_to_id":null,"content":"I'd like to see this after the plot with the individual senses","create_time":"$D2023-06-14T09:34:30.129Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"372ac05cf2ae2d66","reply_to_id":null,"content":"replace email for feedback?","create_time":"$D2023-06-28T08:51:18.978Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"11562f18aa92c1d4","reply_to_id":"372ac05cf2ae2d66","content":"also, do we want to list all contributors with roles somewhere?","create_time":"$D2023-06-28T08:53:49.531Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"fa96c8432610c014","reply_to_id":null,"content":"I don't seem to be able to see anything when I click on a year on the timeline. Also, I'm not 100% clear what \"on the line of text from an individual article\" means.","create_time":"$D2023-06-28T08:55:25.052Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"2a7e7ab24223efef","reply_to_id":null,"content":"I wonder if it would make sense to group together the counties into broader sets (e.g. North, South) as well, in addition to the individual counties?","create_time":"$D2023-06-28T08:56:50.324Z","update_time":"$D2023-06-28T09:09:43.522Z","resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"cc3a303a554708d9","reply_to_id":"5dcef772f4e09918","content":"should we say somewhere that we only retain the annotations with agreement >=0.66 in our subsequent analysis?","create_time":"$D2023-06-28T08:57:41.789Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"a113a87abf4f62da","reply_to_id":"46a22bdc96c0c184","content":"should we explain what \"live\" and \"visible\" mean in Zooniverse?","create_time":"$D2023-06-28T08:58:07.105Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"2f403a6c8cc101a7","reply_to_id":null,"content":"explain that this refers to our annotation dataset","create_time":"$D2023-06-28T08:58:35.265Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"e9139efb5f7bd51b","reply_to_id":null,"content":"or rather in our specific annotated dataset?","create_time":"$D2023-06-28T08:58:53.527Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"6ac532ef9c926475","reply_to_id":"372ac05cf2ae2d66","content":"agreed at meeting to add Barbara's email","create_time":"$D2023-06-28T09:08:14.058Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"11abd44db871b61d","reply_to_id":"5dcef772f4e09918","content":"done by default but agreement rate can be changed - agreed KDL to add","create_time":"$D2023-06-28T09:11:47.898Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"d5b5d6f3089eb015","reply_to_id":"46a22bdc96c0c184","content":"agreed to link to glossary but need explanation by Mia","create_time":"$D2023-06-28T09:13:43.632Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"31b19da9abbdf1b8","reply_to_id":"5dcef772f4e09918","content":"Added below - please review and resolve if fine.","create_time":"$D2023-07-04T14:37:32.804Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"142a010d005362d1","reply_to_id":"fa96c8432610c014","content":"The line of text refers to the text snippet that shows on the table. If you click on it you can see the original image.","create_time":"$D2023-07-04T14:52:05.593Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"882d976733b00442","reply_to_id":"5dcef772f4e09918","content":"I don't think this is needed at this point in the notebook anymore as information about which annotations are kept is described earlier in the notebook.","create_time":"$D2023-07-05T07:53:00.381Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"806305b2def84696","reply_to_id":"2f403a6c8cc101a7","content":"This one is actually about all the data in the press directory, the one below is the project specific one.","create_time":"$D2023-07-05T13:52:13.326Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"940d4b4889edf5ac","reply_to_id":null,"content":"this text needs changing: we should add \"King's College London\" to the list of universities.","create_time":"$D2023-07-07T09:36:11.828Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"0fea091849082558","reply_to_id":"940d4b4889edf5ac","content":"You should be able to edit at https://observablehq.com/@jmiguelv/lwm-backend-text, please let me know if you are having issues.","create_time":"$D2023-07-10T17:27:21.699Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"2ac7085512c3ec08","reply_to_id":"940d4b4889edf5ac","content":"it worked!","create_time":"$D2023-07-10T17:57:03.449Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"c70ec996c44b9944","reply_to_id":null,"content":"Discussed with Barbara - she agrees nice to keep but might need to reduce and explain in text","create_time":"$D2023-07-13T08:43:15.372Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"001ead51f9ca0d7c","reply_to_id":"c70ec996c44b9944","content":"Added text in italic but please refine if needed. Can I confirm what to reduce please? Size, number, speed?","create_time":"$D2023-07-13T08:53:56.702Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ae44428717bed65","login":"ongtiffany","name":"Tiffany Ong","avatar_url":"https://avatars-next.observableusercontent.com/90bb82ebec84e2a6c0c37b437ab558ae373abc2059b6c334a1b176809bc79a50","tier":null}},{"id":"1a0dc1c9a0b42da6","reply_to_id":"c70ec996c44b9944","content":"speed please - text changed","create_time":"$D2023-07-13T09:36:54.434Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"3e69ea70efb542c5","reply_to_id":null,"content":"I'm not sure what the grey lines represent. Also, what is the y axis?","create_time":"$D2023-07-14T08:35:51.755Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"56e4cc4234c2d079","reply_to_id":null,"content":"this is very cool! But is there a way to display the main change point? In other words, when did one mean take over the other one? Is it possible to explain this?\nAlso, would it be possible to include the changepoint detection method used in Nilo's code?","create_time":"$D2023-07-14T08:37:32.686Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"b4a2b91b2a8957d9","reply_to_id":null,"content":"so if I look at bike, I don't see any changes in meaning, right? would it be possible to connect this table with the plots above so that the users can see a clear (as much as possible!) story? Maybe the table needs to be moved further up to best interpret the plots?","create_time":"$D2023-07-14T08:39:51.675Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"dd705ae9dcaeed41","reply_to_id":"3e69ea70efb542c5","content":"Expanded the explanation.","create_time":"$D2023-07-14T11:46:05.001Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"8cd9f8cffa992e3b","reply_to_id":"b4a2b91b2a8957d9","content":"Yes, that is true for bike. Please see the other changes and let me know if you still think this should be moved up.","create_time":"$D2023-07-14T11:51:02.563Z","update_time":"$D2023-07-14T11:51:15.746Z","resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"b8205df626eb8692","reply_to_id":"989a9f255a9444eb","content":"Made minor changes to last paragraph","create_time":"$D2023-07-14T12:14:58.204Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"b2ec398dbbfd640d","login":"aciula","name":"Arianna Ciula","avatar_url":"https://avatars-next.observableusercontent.com/57b045e4ad74d8a896a5152d0f0726fb2b1f1acd5b126df478c14298878c86cd","tier":null}},{"id":"25dde4801a19d80c","reply_to_id":"989a9f255a9444eb","content":"Changed bicycle back to bike :)","create_time":"$D2023-07-14T13:54:14.926Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"6ad17e450c8ae9be","reply_to_id":null,"content":"Apologies @barbaramcg, I accidentally removed your comment!\n\nBarbara McGillivrayJul 14 08:37 UTC\nthis is very cool! But is there a way to display the main change point? In other words, when did one mean take over the other one? Is it possible to explain this?\nAlso, would it be possible to include the changepoint detection method used in Nilo's code?","create_time":"$D2023-07-14T14:20:56.459Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"85b8e144f08120f7","reply_to_id":"6ad17e450c8ae9be","content":"With the current method it picks up change points based on the threshold, not necessarily a unique one. The method Nilo used was from a Python package, not sure if there is a version for JavaScript, but will look around to see how feasible it would be to port it.\n\nThe plot below this one shows when meanings take over.","create_time":"$D2023-07-14T14:23:28.110Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"df0b152433340664","reply_to_id":"b4a2b91b2a8957d9","content":"ok, so bike and coach didn't change, but the other two did, interesting. the text is clearer now so the table can stay where it is, in my opinion","create_time":"$D2023-07-14T15:09:44.568Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}},{"id":"80394ba5705339df","reply_to_id":"6ad17e450c8ae9be","content":"@barbaramcg, I have implemented a port of the PELT algorithm in JavaScript, would you like the notebook to be updated to use it instead of CUSUM for change point detection?","create_time":"$D2023-07-20T22:28:34.497Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8ed700ef90ca11f2","login":"jmiguelv","name":"Miguel Vieira","avatar_url":"https://avatars-next.observableusercontent.com/4fba40b1cfe58889e137e945ed99c3eef4bd8f6f4585d123d0288455e42e1195","tier":null}},{"id":"a2f42941b6c2d150","reply_to_id":"6ad17e450c8ae9be","content":"yes, I think that would make it easier to compare the results of Nilo's code. If possible and if it doesn't take too much space, you could include the results of both PELT and CUSUM. Thanks!","create_time":"$D2023-08-08T14:46:07.802Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"17a97e184c882be3","login":"barbaramcg","name":"Barbara McGillivray","avatar_url":"https://avatars-next.observableusercontent.com/7c65ca260c4d0da1a946b5e79c1296fdb79b0a2e42a19dc6a6d901d29a997300","tier":null}}],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":4,"collection":null,"type":"notebook","body":{"title":"Language of Mechanisation","cells":[{"id":0,"value":"# Language of Mechanisation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2362,"value":"disclaimer(\n \"contact [Barbara McGillivray](mailto:barbara.mcgillivray@kcl.ac.uk)\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4494,"value":"livingWithMachines","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":83,"value":"$30","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4497,"value":"alert(\n \"Note\",\n `The data used in this notebook was exported / uploaded at ${dataExportDate}.`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4259,"value":"toc()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5311,"value":"## Word case studies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2227,"value":"\n### 1. Select a word\nSee how the selected word meaning changed over time and place. **The rest of the notebook (sections 2-5) will change according to the selection**, with options to filter data and allow more exploration. \n\n_Icons from [Noun Project](https://thenounproject.com/) CC BY 3.0 exemplify the change in meanings; they are not necessarily historically accurate._\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2752,"value":"viewof selectedWorkflow = Inputs.radio(workflows, {\n format: (d) =>\n html`${\n gifMap[d.word]\n }${\n d.word\n }`,\n value: workflows[0]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":406,"value":"**${workflow.word}** - In the workflow _${workflow.display_name}_, volunteers were asked to classify the articles into the meanings listed in section 2 below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4628,"value":"### 2. Select meanings \nThe first ${digitMap[defaultDomain.length]} potential meanings are selected by default as they represent the word meanings we want to analyse; however, all options of how the articles could be classified are available for selection.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2232,"value":"viewof selectedDomains = interactiveLegend(Object.keys(workflowDomain), {\n range: Object.values(workflowDomain),\n display: \"block\",\n initial: defaultDomain\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4995,"value":"### 3. Select annotation agreement\nEach article is classified by multiple volunteers, and it is possible for there to be disagreements among them. Adjusting the slider value modifies the required level of agreement (set by default at ${agreementThreshold}%, corresponding to 2 annotators out of 3 agreeing with each other). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4781,"value":"#### Annotations agreement distribution\nThe plot displays a histogram of agreement rates, where each blue square section represents the count of occurrences within a specific range of agreement rates. The **vertical line** shows the selected agreement rate corresponding to the value marked in the slider.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2774,"value":"viewof agreementThreshold = Inputs.range([0, 100], {\n label: md`**Agreement %**`,\n step: 5,\n value: 65\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2573,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5327,"value":"There are **${_.uniq(annotations.map(a => a.sid)).length.toLocaleString()}** individual articles—corresponding to **${agreement.filter(a => a.rate > agreementThreshold / 100).length.toLocaleString()}** annotations in the graph above—with an agreement rate greater or equal to **${agreementThreshold}%** ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":5332,"value":"### 4. Explore the data\nHovering over a data point, will show **detailed information** such as the number of annotations, the percentage ratio of annotations, and the minimum and maximum confidence levels. By clicking on a data point, a table opens up to display the underlying data and offering the option to view the **original article's image** for further analysis.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":515,"value":"#### Absolute number of annotations\nThis plot displays the total count of annotations for each word meaning in each year, showing the raw number without any adjustments or calculations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1329,"value":"subjectsTable(selectedTitle.workflow, workflowSubjects, selectedSids.workflow)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":507,"value":"{\n const data = annotationsByYear.filter((a) =>\n selectedDomains.includes(a.annotation)\n );\n const domain = Object.fromEntries(\n Object.entries(workflowDomain).filter(([k, v]) =>\n selectedDomains.includes(k)\n )\n );\n\n const plot = ridgelinePlot(data, {\n x: \"year\",\n xDomain: yearDomain,\n y: \"count\",\n fy: \"annotation\",\n domain,\n sort: \"year\",\n title: (d) => title(d),\n height: selectedDomains.length * 75,\n overlap: 1\n });\n\n handlePlotClick(\n plot,\n data,\n workflowAnnotations,\n \"annotation\",\n (title, sids) => {\n mutable selectedTitle = {\n ...selectedTitle,\n workflow: title\n };\n mutable selectedSids = {\n ...selectedSids,\n workflow: sids\n };\n }\n );\n\n return plot;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3459,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":3904,"value":"subjectsTable(selectedTitle.norm, workflowSubjects, selectedSids.norm)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3228,"value":"{\n const data = annotationsByYear.filter((a) =>\n selectedDomains.includes(a.annotation)\n );\n\n const plot = facetedLinePlot(data, {\n x: \"year\",\n y: \"norm\",\n fy: \"annotation\",\n xDomain: yearDomain,\n fyDomain: workflowDomain,\n title: (d) => title(d),\n r: \"count\",\n colourTrendLines: false,\n showConfidenceInterval: true,\n showLinearRegression: true\n });\n\n handlePlotClick(\n plot,\n data,\n workflowAnnotations,\n \"annotation\",\n (title, sids) => {\n mutable selectedTitle = {\n ...selectedTitle,\n norm: title\n };\n mutable selectedSids = {\n ...selectedSids,\n norm: sids\n };\n }\n );\n\n return plot;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4143,"value":"#### Word meanings per year, county, region and politics\nIn this section, we can explore the evolution of word meanings based on various factors such as year, county, region, and newspaper politics.\n\nBy using the __Meanings spanning a minimum of years__ filter, we can exclude locations (identified by county or region) or politics that do not feature in the data for the specified number of years. This allows us to focus on meaningful trends and patterns that emerge over time.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4085,"value":"viewof optionsMeaningsBy = Inputs.form({\n facetBy: viewof facetBy,\n facetMinYears: viewof facetMinYears,\n selectedFacets: viewof selectedFacets,\n facetNormaliseToggle: viewof facetNormaliseToggle,\n facetRegressionToggle: viewof facetRegressionToggle,\n facetConfidenceToggle: viewof facetConfidenceToggle\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4864,"value":"Plot.legend({\n color: {\n domain: selectedDomains,\n legend: true,\n range: Object.entries(workflowDomain)\n .filter(([k, _]) => selectedDomains.includes(k))\n .map(([_, v]) => v)\n },\n columns: selectedDomains.length > 2 ? 2 : 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2792,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6708,"value":"### 5. Semantic change of ${workflow.word}\nThis section explores when the meaning of the word **${workflow.word}** changed across decades. We are using decades in this section to group counts and hence reduce the variation in the data and also as a comparison with the [work previously done](https://aclanthology.org/2022.nlp4dh-1.12.pdf) by the some members of the team.\n\nThe plot below shows the potential decades for when the word **${workflow.word}** changed meaning. The shifts are calculated using [penalised change point detection](https://centre-borelli.github.io/ruptures-docs/user-guide/detection/pelt/). The vertical black lines mark the specific location or time point where the change occurs.\n\nBoth the **cumulative sum window** and the change point detection **penalty** can be adjusted to tune the sensitivity of detecting change points. A smaller window and lower penalty will detect more potential change points, while a larger window and higher penalty will detect only major shifts.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":6290,"value":"Inputs.bind(\n Inputs.radio(workflows, { format: (d) => html`${d.word}` }),\n viewof selectedWorkflow\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5880,"value":"viewof cumsumWindow = Inputs.range([1, decades.length], {\n label: \"Cumulative sum window\",\n step: 1,\n value: 2\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7004,"value":"viewof penalty = Inputs.range([0, 40], { label: \"Penalty\", step: 1, value: 25 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5850,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6560,"value":"#### Meaning trajectory of ${workflow.word}\nThis plot illustrates the trajectory of meanings associated with the word **${workflow.word}** over time. Each row in the plot corresponds to a specific meaning, and the dots placed along the rows represent the most prevalent meaning during the corresponding decade. The arrow connects the first meaning with the last.\n\nThe word ${workflow.word} ${didMeaningShift ? `appears to have changed in meaning **${digitMap[meaningsShift.length - 1]}** ${pluralize(\"time\", meaningsShift.length)}` : 'did not change in meaning'} according to the annotations.\n- ${meaningsShift\n .map(\n (d) =>\n `**${d.decade}**: **${d.meaning}**`\n )\n .join(\"\\n- \")}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":6467,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6401,"value":"##### Changes in the meaning of ${workflow.word} data\nThis table represents the data regarding changes in the meaning of the word **${workflow.word}** over time. The columns provide the following information:\n\n- **Decade**: This column indicates the value of each decade.\n- **Meaning**: This column specifies the most prevalent meaning associated with **${workflow.word}** during a particular decade.\n- Individual Meaning Columns: These columns represent the cumulative sum of occurrences for each meaning, calculated using a ${digitMap[cumsumWindow]}-decade window.\n\nPlease note that the table showcases the changes in meaning and the cumulative sums, taking into account a ${digitMap[cumsumWindow]}-decade window to capture shifts in meaning over time.\n\n${DOM.download(new Blob([d3.csvFormat(cumsumData)], { type: \"text/csv\" }), `cumsumData_${workflow.word}_w${cumsumWindow}.csv`, \"Download the data\")}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":6795,"value":"cumsumData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4508,"value":"## Data sources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4505,"value":"aboutTheData(projectTitle, totalNumberOfSubjects)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3863,"value":"## Explore the data sources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":925,"value":"### Newspapers\nAll data points come from Mitchells Press Directories information processed by the Living with Machines project.\n\nLooking more in depth into the newspapers' data, we can compare the price range, political leaning, their locations and year of publication...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1033,"value":"newspapersPoliticalDistribution(getNewspapersPoliticalDistribution())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1088,"value":"#### Political groups of the _${projectTitle}_ workflows \nThis project worked with digitised newspapers in the public domain, a proportion of digitised newspapers available on the British Newspaper Archive, which is a proportion of all newspapers published.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1624,"value":"#### Explore the newspaper records used in the _${projectTitle}_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":292,"value":"viewof sourcesGroupBy = Inputs.radio(\n [\"county\", \"politics\", \"price\", \"price_range\", \"title\", \"year\"],\n {\n label: \"Group by:\",\n value: \"politics\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":321,"value":"viewof sourcesColourBy = Inputs.radio(\n [\"county\", \"politics\", \"price\", \"price_range\", \"year\"],\n {\n label: \"Colour by:\",\n value: \"price_range\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1091,"value":"#### Overview of Political groups\nOverview of political leaning for newspaper titles according to [Mitchell's Press Directories](https://en.wikipedia.org/wiki/Mitchell%27s_Press_Directories), a contemporary source that noted the politics of a newspaper title and its proprietors.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":216,"value":"newspapersPlot(sources, sourcesGroupBy, sourcesColourBy)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1588,"value":"newspapersPoliticalDistribution(\n sourcesPolitics,\n Object.fromEntries(\n Object.entries(domains.politics).filter(([key]) =>\n sourcesPolitics.map((s) => s.politics).includes(key)\n )\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4808,"value":"### Crowdsourced classifications and tasks\nIn Zooniverse, the volunteers were asked to perform a group of tasks (**workflows**) of which there were 4:\n- ${workflows.map((w) => w.display_name).join(\"\\n- \")}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4812,"value":"The bar chart below shows the number of articles each year from ${_.minBy(subjects, \"year\").year} to ${_.maxBy(subjects, \"year\").year}, and can be coloured by workflow. The years, ${getSubjectTopYears(2).join(\" and \")}, have the most subjects in the data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2422,"value":"viewof toggleSubjectsColour = Inputs.toggle({\n label: \"Colour by workflow\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2425,"value":"toggleSubjectsColour\n ? subjectsPlot(subjects, \"workflow\")\n : subjectsPlot(subjects, \"darkgrey\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4805,"value":"### Zooniverse Volunteers\nThere were ${(participantsBreakdown.loggedIn + participantsBreakdown.notLoggedIn).toLocaleString()} individual contributors that used the Zooniverse platform. Just under half (${participantsBreakdown.notLoggedIn.toLocaleString()}) did not log into a Zooniverse account - this shows the value of allowing users to contribute without an account.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":194,"value":"workflowsParticipantsPlot(projectParticipants)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4821,"value":"Each workflow had a number of tasks which the volunteers were asked to complete by subject classification (**annotations**). In total there were ${totalNumberOfAnnotations.toLocaleString()} annotations over ${totalNumberOfSubjects.toLocaleString()} subjects across the ${workflows.length} workflows.\n\n${activeStatusDescription}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":34,"value":"workflowsAnnotationsPlot(workflows)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4547,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4588,"value":"## Terminology\n\nIn this notebook we will use the following terms:\n\n**Volunteers** are those who contributed to our tasks in Zooniverse by annotating segments of newspaper articles.\n\n**Articles** are the images containing segments of newspaper articles annotated by the volunteers on Zooniverse.\n\n**Annotations** are the articles which have been annotated by volunteers on Zooniverse.\n\n**Workflows** are groups of tasks which we asked volunteers to perform. These consisted in annotating articles containing the four words of interest (_trolley_, _car_, _coach_, and _bicycle_).\n\n${agreementDescription}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":5430,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4562,"value":"accessibility(\"[Barbara McGillivray](mailto:barbara.mcgillivray@kcl.ac.uk)\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4568,"value":"observable","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4560,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4549,"value":"## The code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1229,"value":"### Views","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1449,"value":"mutable selectedTitle = ({\n agreement: \"\",\n norm: \"\",\n workflow: \"\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1134,"value":"mutable selectedSids = ({\n agreement: [],\n norm: [],\n workflow: []\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2818,"value":"title = (d) =>\n `${d.annotation}, ${d.year}\\n${d.count} annotations\\n${d3.format(\"0.2%\")(\n d.norm\n )}, [${d3.format(\"0.2%\")(d.lower)}, ${d3.format(\"0.2%\")(d.upper)}]`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4089,"value":"viewof facetBy = Inputs.radio([\"year\", \"county\", \"region\", \"politics\"], {\n format: (d) => html`${d}`,\n label: \"Group by\",\n value: \"year\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4226,"value":"viewof facetMinYears = Inputs.range([1, _.subtract(yearDomain)], {\n disabled: facetBy === \"year\",\n label: \"Meanings spanning a minimum of years\",\n step: 1,\n value: 10\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4106,"value":"viewof facetNormaliseToggle = Inputs.toggle({\n label: \"Relative frequencies\",\n value: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4109,"value":"viewof facetRegressionToggle = Inputs.toggle({\n disabled: !facetNormaliseToggle,\n label: `Display trend line${\n facetNormaliseToggle ? \"\" : \" (enable normalised counts first)\"\n }`,\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4113,"value":"viewof facetConfidenceToggle = Inputs.toggle({\n disabled: !facetNormaliseToggle,\n label: `Display uncertainty${\n facetNormaliseToggle ? \"\" : \" (enable normalised counts first)\"\n }`,\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4772,"value":"viewof selectedFacets = {\n const label = `Select ${pluralize(facetBy, 2)}`;\n const values = _.sortBy(Object.keys(countsByFacet));\n const view = Inputs.checkbox(values, {\n disabled: values.filter((v) => countsByFacet[v] < facetMinYears),\n format: (d) => html`${d}`,\n label,\n value: values.filter((v) => countsByFacet[v] >= facetMinYears)\n });\n\n return facetBy !== \"year\" ? view : Inputs.checkbox({ disabled: true });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":65,"value":"### Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":3117,"value":"#### Domain","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4923,"value":"gifMap = ({ Bike: bike, Car: car, Coach: coach, Trolley: trolley })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4949,"value":"gifProperties = ({\n style: \"vertical-align: middle;\",\n width: 40\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4920,"value":"bike = FileAttachment(\"bike@1.gif\").image(gifProperties)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4936,"value":"car = FileAttachment(\"car@1.gif\").image(gifProperties)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4937,"value":"coach = FileAttachment(\"coach@1.gif\").image(gifProperties)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4938,"value":"trolley = FileAttachment(\"trolley@1.gif\").image(gifProperties)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2247,"value":"defaultDomain = Object.keys(workflowDomain).slice(\n 0,\n Object.keys(workflowDomain).indexOf(\"other\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2236,"value":"workflowDomain = domains[domainKey] ||\n _(workflowAnnotations.map((a) => a.annotation))\n .uniq()\n .sortBy()\n .reduce((acc, w, i) => {\n acc[w] = defaultColourScheme[i];\n return acc;\n }, {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3928,"value":"yearDomain = [\n // _.minBy(workflowAnnotations, \"year\").year,\n 1810,\n // _.maxBy(workflowAnnotations, \"year\").year\n 1920\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2835,"value":"domainKey = selectedWorkflow.display_name","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4664,"value":"digitMap = ({\n 0: \"zero\",\n 1: \"one\",\n 2: \"two\",\n 3: \"three\",\n 4: \"four\",\n 5: \"five\",\n 6: \"six\",\n 7: \"seven\",\n 8: \"eight\",\n 9: \"nine\",\n 10: \"ten\",\n 11: \"eleven\",\n 12: \"twelve\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3119,"value":"#### Changepoint detection","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":7001,"value":"Inputs.bind(\n Inputs.radio(workflows, { format: (d) => html`${d.word}` }),\n viewof selectedWorkflow\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6762,"value":"didMeaningShift = meaningsShift.length > 1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6766,"value":"meaningsShift = meaningsData.reduce((acc, cur, idx) => {\n if (idx === 0) {\n acc.push(cur);\n return acc;\n }\n\n const prev = meaningsData[idx - 1];\n\n if (prev.meaning !== cur.meaning) {\n acc.push(cur);\n }\n\n return acc;\n}, [])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6680,"value":"meaningsData = cumsumData.filter((d) => d.meaning !== \"no data\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7030,"value":"##### PELT","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":6944,"value":"decades.filter((_, i) => peltChangePoints.includes(i))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6860,"value":"peltChangePoints = pelt(peltData, penalty).filter(\n (d) => d !== 0 && d !== peltData.length\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6849,"value":"peltData = {\n const data = annotationsByDecade(cumsumWindow);\n const field = `cumsumW${cumsumWindow}`;\n\n return Object.values(\n annotationsByDecade(cumsumWindow).reduce((acc, cur) => {\n acc[cur.decade] = acc[cur.decade] || { decade: cur.decade, values: [] };\n acc[cur.decade].values.push(cur[field]);\n return acc;\n }, {})\n ).map((d) => d.values);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6851,"value":"pelt = (data, penalty) => {\n const n = data.length;\n const cost = new Array(n + 1).fill(0);\n const lastChangepoint = new Array(n + 1).fill(0);\n\n for (let t = 1; t <= n; t++) {\n let minCost = Infinity;\n for (let s = 1; s <= t; s++) {\n const currCost = l1Cost(data, s, t) + penalty + cost[s - 1];\n if (currCost < minCost) {\n minCost = currCost;\n lastChangepoint[t] = s;\n }\n }\n cost[t] = minCost;\n }\n\n // backtrack to find the changepoints\n let changepoints = [];\n let t = n;\n while (t > 0) {\n const s = lastChangepoint[t];\n changepoints.unshift(s - 1);\n t = s - 1;\n }\n\n return changepoints;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6845,"value":"l1Cost = (data, start, end) => {\n const segment = data.slice(start, end);\n\n // calculate the median for each column (axis=0)\n const medians = d3.transpose(segment).map((column) => d3.median(column));\n\n // calculate the absolute differences and their sum\n return _.sum(\n segment.map((row) =>\n _.sum(row.map((val, index) => Math.abs(val - medians[index])))\n )\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7038,"value":"##### CUSUM","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":6205,"value":"viewof shiftThreshold = Inputs.range([1, 100], {\n label: \"Threshold\",\n step: 1,\n value: 10\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6189,"value":"getChangePoints = (data, field, threshold) => {\n const changePoints = [];\n\n for (let i = 1; i < data.length; i++) {\n const currentValue = data[i][field];\n const previousValue = data[i - 1][field];\n const difference = currentValue - previousValue;\n const absChange = Math.abs(difference);\n\n if (absChange > threshold) {\n changePoints.push({\n decade: data[i].decade,\n meaning: data[i].meaning,\n cur: currentValue,\n prev: previousValue,\n change: difference,\n absChange\n });\n }\n }\n\n return changePoints;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6353,"value":"cumsumData = {\n const data = annotationsByDecade(cumsumWindow);\n const field = `cumsumW${cumsumWindow}`;\n\n return Object.values(\n annotationsByDecade(cumsumWindow).reduce((acc, cur) => {\n acc[cur.decade] = acc[cur.decade] || { decade: cur.decade };\n acc[cur.decade].meaning =\n _.maxBy(\n data.filter((d) => d.decade === cur.decade && d[field] > 0),\n field\n )?.meaning || \"no data\";\n acc[cur.decade][cur.meaning] = cur[field];\n return acc;\n }, {})\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6996,"value":"#### Annotations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4217,"value":"countsByFacet = _.countBy(annotationsBy[facetBy], facetBy)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5829,"value":"annotationsByDecade = (window = 2) => {\n const countField = \"norm\";\n\n return decades.flatMap((decade) =>\n defaultDomain.map((domain) => {\n const decadeData = annotationsByYear.filter(\n (a) =>\n a.annotation === domain && a.year >= decade && a.year <= decade + 9\n );\n\n return {\n decade: decade,\n meaning: domain,\n sum: _.sumBy(decadeData, countField) || -1,\n cumsum: _.sumBy(\n annotationsByYear.filter(\n (a) => a.annotation === domain && a.year <= decade + 9\n ),\n countField\n ),\n [`cumsumW${window}`]: _.sumBy(\n annotationsByYear.filter(\n (a) =>\n a.annotation === domain &&\n a.year >= decade - (window - 1) * 10 &&\n a.year <= decade + 9\n ),\n countField\n )\n };\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6372,"value":"decades = d3.range(1800, 1920, 10)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":483,"value":"annotationsByYear = annotationsBy[\"year\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4134,"value":"annotationsBy = ({\n year: getAnnotationsBy(),\n county: getAnnotationsBy([\"year\", \"county\"]),\n region: getAnnotationsBy([\"year\", \"region\"]),\n politics: getAnnotationsBy([\"year\", \"politics\"])\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4043,"value":"function getAnnotationsBy(keys = [\"year\"]) {\n const countBy = {};\n const totalCountsBy = {};\n\n for (const annotation of annotations) {\n const key = keys.map((k) => annotation[k]).join(\"-\");\n const annotationKey = `${annotation.annotation}-${key}`;\n\n if (!countBy[annotationKey]) {\n countBy[annotationKey] = { ...annotation, count: 0 };\n }\n\n countBy[annotationKey].count += 1;\n\n totalCountsBy[key] = (totalCountsBy[key] || 0) + 1;\n }\n\n const data = Object.values(countBy).sort((a, b) => a.year - b.year);\n const normalisedCounts = data.map((d) => {\n const key = keys.map((k) => d[k]).join(\"-\");\n const norm = d.count / totalCountsBy[key];\n return {\n ...d,\n norm,\n ...confidenceInterval(norm, totalCountsBy[key])\n };\n });\n\n return normalisedCounts;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3112,"value":"/**\n * Calculates the confidence interval for a proportion.\n * The confidence interval is calculated at a 95% confidence level.\n * https://www.statology.org/confidence-interval-proportion/\n *\n * @param {number} sampleProportion - The proportion (between 0 and 1) from the sample.\n * @param {number} sampleSize - The size of the sample.\n * @returns {object} An object containing the lower and upper bounds of the confidence interval.\n */\nconfidenceInterval = (sampleProportion, sampleSize) => {\n // z-score for 95% confidence level\n const zScore = 1.96;\n\n // calculate the standard error of the proportion\n const standardError = Math.sqrt(\n ((1 - sampleProportion) * sampleProportion) / sampleSize\n );\n\n // calculate the lower and upper bounds of the confidence interval\n const lower = sampleProportion - zScore * standardError;\n const upper = sampleProportion + zScore * standardError;\n\n return {\n lower,\n upper\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2487,"value":"annotations = {\n const ratios = Object.fromEntries(\n getAgreement(workflowAnnotations).map((a) => [a.sid, a.rate])\n );\n\n return workflowAnnotations\n .filter((a) => selectedDomains.includes(a.annotation))\n .filter((a) => ratios[a.sid] >= agreementThreshold / 100);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5396,"value":"agreementStroke = \"#f28e2c\" //\"#edc949\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7089,"value":"agreementAvg = {\n const agreement = Object.values(\n workflows\n .flatMap((w) =>\n _.uniqBy(\n getAgreement(prepareAnnotations(w)).map((a) => ({\n key: `${a.rate}-${a.freq}`,\n rate: a.rate,\n freq: a.freq\n })),\n \"key\"\n )\n )\n .reduce((accumulator, currentValue) => {\n const key = currentValue.rate;\n\n if (!accumulator[key]) {\n accumulator[key] = {\n rate: currentValue.rate,\n freq: 0\n };\n }\n\n accumulator[key].freq += currentValue.freq;\n\n return accumulator;\n }, {})\n );\n\n // return agreement;\n\n const totalRateSum = agreement.reduce((sum, currentValue) => {\n return sum + currentValue.rate * currentValue.freq;\n }, 0);\n\n const totalFreq = agreement.reduce((sum, currentValue) => {\n return sum + currentValue.freq;\n }, 0);\n\n return totalRateSum / totalFreq;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7149,"value":"agreement = getAgreement(prepareAnnotations(workflow))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1919,"value":"getAgreement = (annotations) => {\n const agreement = _(annotations)\n .groupBy(\"sid\")\n .map((group) => {\n const total = _.sumBy(group, \"annotation_n\");\n const max = _.maxBy(group, \"annotation_n\").annotation_n;\n const rate = parseFloat((max / total).toFixed(2));\n return { sid: group[0].sid, rate };\n })\n .sortBy(\"rate\")\n .value();\n\n return agreement.map((a) => ({\n ...a,\n freq: _.countBy(agreement, \"rate\")[a.rate]\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3103,"value":"getAnnotationCount = (annotation) => workflowAnnotationsCount[annotation]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3089,"value":"workflowAnnotationsCount = _.countBy(workflowAnnotations, \"annotation\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":374,"value":"workflowAnnotations = prepareAnnotations(workflow)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7119,"value":"prepareAnnotations = (workflow) =>\n getWorkflowAnnotations(workflow.workflow_id, [\"T0\"])\n .map((a) => {\n const subject = getSubjects(workflow.workflow_id).find(\n (s) => s.sid === a.sid\n );\n const source = newspapers[subject.nlp];\n\n return { ...a, subject, source };\n })\n .filter((a) => a.source)\n .flatMap((a) =>\n Object.entries(a.T0).map(([k, v]) => ({\n sid: a.sid,\n annotation: mapAnnotation(k),\n annotation_n: v,\n year: a.subject.year,\n county: a.source.county,\n region: getRegion(a.source.county),\n politics: a.source.politics,\n price: a.source.price,\n price_range: a.source.price_range,\n cc: a.subject.cc,\n rr: a.subject.rr\n }))\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3092,"value":"mapAnnotation = (value) => {\n const map = {\n \"a railway carriage or wagon, usually powered by steam\":\n \"a railway carriage or wagon\",\n \"a road vehicle powered by a combustion engine, usually for a driver and a small number of passengers\":\n \"a road vehicle powered by a motor\"\n };\n\n if (value in map) {\n return map[value];\n }\n\n if (value.indexOf(\"other\") >= 0) {\n return \"other\";\n }\n\n return value;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2725,"value":"#### Workflow","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":418,"value":"workflowSubjects = getSubjects(workflow.workflow_id)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":372,"value":"workflow = selectedWorkflow","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1381,"value":"#### Newspapers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1590,"value":"sourcesPolitics = getNewspapersPoliticalDistribution(sources)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":204,"value":"sources = getNewspapers(nlps)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":999,"value":"newspapers = getNewspapers()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1383,"value":"#### Project","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":114,"value":"participantsBreakdown = getParticipantsBreakdown(workflows)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":131,"value":"projectParticipants = workflows.flatMap((w) => getParticipants(w.workflow_id))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":96,"value":"totalNumberOfSubjects = [...new Set(subjects.map((s) => s.sid))].length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":91,"value":"totalNumberOfAnnotations = workflows.reduce((acc, cur) => acc + cur.classifications_count, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":980,"value":"nlps = [...new Set(subjects.map((s) => s.nlp))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4815,"value":"getSubjectTopYears = (n = 2) => {\n return _(subjects)\n .countBy(\"year\")\n .entries()\n .sort((a, b) => b[1] - a[1])\n .slice(0, n)\n .map(([year, _]) => Number(year))\n .value();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":977,"value":"subjects = workflows\n .flatMap((w) => [...getSubjects(w.workflow_id)])\n .map((s) => ({ ...s, workflow: getWorkflow(s.workflow_id).display_name }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":22,"value":"workflows = project.workflows\n .map((w) => getWorkflow(w.workflow_id))\n .map((w) => ({\n ...w,\n word:\n w.display_name.indexOf(\"Bic\") >= 0\n ? \"Bike\"\n : _.startCase(w.display_name.match(/'(.*?)'/)[1])\n }))\n .sort((a, b) =>\n a.word.indexOf(\"B\") >= 0 ? 1 : a.workflow_id > b.workdlow_id\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"project = getProject(projectTitle)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":11,"value":"projectTitle = \"Language of Mechanisation\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1385,"value":"### Dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2364,"value":"import {\n aboutTheDataMechanisation as aboutTheData,\n accessibility,\n activeStatusDescription,\n agreementDescription,\n alert,\n participantsDescription,\n disclaimer,\n livingWithMachines,\n observable,\n toc\n} from \"@jmiguelv/lwm-backend-text\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2372,"value":"import {\n defaultColour,\n defaultColourScheme,\n domainComparator,\n domains,\n facetedLinePlot,\n formatYear,\n handlePlotClick,\n interactiveLegend,\n newspapersPlot,\n newspapersPoliticalDistribution,\n ridgelinePlot,\n subjectsPlot,\n subjectsTable,\n workflowsAnnotationsPlot,\n workflowsParticipantsPlot\n} from \"@jmiguelv/lwm-backend-plotting\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":202,"value":"import {\n getNewspapers,\n getNewspapersPoliticalDistribution\n} from \"@jmiguelv/lwm-backend-data\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5,"value":"import {\n dataExportDate,\n getParticipants,\n getParticipantsBreakdown,\n getProject,\n getRegion,\n getSubjects,\n getWorkflow,\n getWorkflowAnnotations\n} from \"@jmiguelv/lwm-backend-data\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":58,"value":"import {\n calculateMarginLeft,\n tooltipPlot\n} from \"@jmiguelv/lwm-backend-plotting\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4342,"value":"pluralize = require(\"pluralize\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6595,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 2f:"$2e:body"