2e:null 2f:["$","title",null,{"children":"Society for Historians of the Early American Republic annual meeting network, 1981-2022 | Maeve Kane | Observable"}] 34:T18b3,Below is a network of panel co-appearances at the SHEAR annual meeting over the last forty years. In preparing my comments for my SHEAR 2022 panel on digital methods in the age of revolutions and thinking about my [new project on early American baptismal networks for the SHEAR second book workshop](https://observablehq.com/@mkane2/baptismal-network-comparison-statistics), it struck me that I didn't have a sense of how the limitations of network analysis would look in my own life. There's also been some discussion the past few years about the status of women in the profession and the tendency of senior or already prominent scholars to cluster together, and I thought I might at least be able to see some change over time on those metrics. So here we are. **The big takeaways:** the conference has only recently approached gender parity between men and women participants, with a major tipping point in 2015; in 1990, 2000, and 2011 there were some historiographic realignments in the field and we appear to be approaching another in 2022; in recent years the conference has had more integration between highly and less highly connected participants. There are some historiographic groupings, but not as much as you'd think, and in 2019 the conference was the most densely interconnected it's ever been. More about all this below. (If you're affiliated with another organization besides SHEAR and you're interested in having this kind of analysis done on your conference programs, feel free to contact me or reuse any of my code with citation.) I got the data for this by downloading and OCR'ing all the programs available on shear.org. There is no program available for 1982, the second year of the conference, and there was no conference in 2020. For years before 2008, the OCR was not great, so I undoubtedly missed some people. (Or included others who shouldn't have been--this is how our beloved colleague Cash Bar showed up in the first version of the network). I grouped together various spellings of names as best I could using OpenRefine in order to catch, for example, James Broussard/James H. Broussard or Elizabeth Covart/Liz Covart on the program in different years. Since gender was part of my question and there's [more than six thousand lines in the full dataset](https://github.com/mkane2/shear-network/blob/main/SHEAR_full.csv), I wasn't going to hand assign gender for everyone. I used a python package to make a probability guess based the occurrence of first names from the program in the US Census, and for the two hundred odd unknowns left over I did my best to assign a gender marker based on publicly available pronouns in faculty profiles or similar. So really, when below I say "women," what I really mean is "people whose names are commonly gendered female" and vice versa for men; this obviously doesn't account for non-binary or other gender identities. If I've misgendered anyone, first, my apologies, and if you'd like me to change how your name or gender is marked here please do reach out and I'll change it. I don't have much to say from this data about race or other demographic composition of the conference, because there's no ethical or realistic way to infer race or Indigenous citizenship status just from names without self-reports. I will just briefly note that, in my own experience, SHEAR seems overwhelmingly white, like many other academic circles. To figure out who was connected to who, I [wrote a python program](https://colab.research.google.com/drive/17tz9-5lNagAoOaA5JrGlJtIUukLYwRrT?usp=sharing) to make relationships between everyone who appeared on the same panel. So for my 2017 panel on gender and family business, Serena Zabin, Sara Lampert, Jessica Blake, and I all had lines drawn between all of us (a chair or commentator appears with no distinction from panelists). By building these connections for each year, the full network is formed. Each individual year's network is not actually that interesting. (You can get an idea of this by looking at the first year of the conference in 1981 in the visualization below--none of the panels are connected to each other because no one appeared on two panels in the same year). The whole network across all forty years of the conference is impossible to read visually. For display and analysis, I took a rolling 7 year window of the network--so when the network for the 2022 conference is shown, it's showing relationships formed at conferences from 2015-2022. Why 7 years? It seemed like a nice number: the average time to a PhD in history according to the AHA, my tenure clock with a maternity leave, or time to write a book. It seemed like a good number for the historiographic network of relationships as people present new work at conferences and think about who around them is working on similar scholarship. (And the network didn't look much different using 5 or 10 years). I then used that 7 year window to run analysis on gender distribution of presenters and relationships and look at how relationships changed over time. I did not include things like presidential addresses, the conference committee, or second book or grad student workshops. This network also doesn't take into account is things like citation networks, attending the same grad program/having the same advisor, just being friends, or things like co-publication. For example, Vanessa Holden and I are working on a textbook together, and we're neither connected in this network nor even in the same programmatically detected community. I've probably had beers with my grad school friend Tom Balcerski more than anyone else in the network, but we're not connected or in the same community; I'm not connected to my students Elisabeth Tatum or Elana Krischer at all. I suspect that a fuller network that included things like citations, co-publications, and co-appearances at other conferences like Omohundro, Native American and Indigenous Studies Association, African American Intellectual History Society, etc, would have clearer historiographic groupings. If you're interested in the gritty details, the Python NetworkX notebook is [available online](https://colab.research.google.com/drive/17tz9-5lNagAoOaA5JrGlJtIUukLYwRrT?usp=sharing). The whole thing can be run in-browser without downloading.35:T566,### Reading a network The physical layout of a network graph does not convey any meaning unless individual dots are connected by a line. Connection by a line indicates that those individuals appeared on the same panel in the seven years prior to the date shown. No difference is made between commenters, chairs, or panelists. When **Number of connections** is selected for "Node size", individuals are sized by the number of connections they have with others in the seven years prior to the year displayed. This is the initial default. When **Network influence** is selected, individuals are sized by a computed measure of how often they are on the shortest path between any two points in the rest of the network. This is a proxy for influence because a larger size with this measure suggests that that individual knows many people who wouldn't otherwise be connected. When the network first loads, individuals will be colored by their **Community**. Communities are programmatically detected. In general, a community within a network means that individuals in that community have more connections with each other than with other members of the wider network. Colors and numbering of communities are arbitrary and have no meaning other than differentiating communities from one another. Gender and bridge status (explained below) can also be selected to color the network.36:Tafc,chart = { const svg = d3.select(this || DOM.svg(width, height)); const g = svg.select("g").empty() ? svg .append("g") .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")") .attr("viewBox", [0, 0, width, height]) : svg.select("g"); let link = g .selectAll(".line") .data(links, (d) => d.source.id + ":" + d.target.id); let node = g.selectAll(".node").data(nodes, (d) => d.id); // transition const dspeed = parseInt(speed / 4); const t = d3.transition().duration(dspeed); const end_t = d3.transition().duration(parseInt(speed / 8)); // enter const linkEnter = link.enter().append("g").attr("class", "line"); const linkLine = linkEnter .append("line") .attr("stroke", "#999") .attr("stroke-opacity", 0.6); node.raise(); const nodeEnter = node .enter() .append("g") .attr("class", "node") .on("mouseover", (event, d) => tooltip_in(event, d)) .on("mouseout", tooltip_out); const nodeCirc = nodeEnter .append("circle") .attr("id", (d) => "node-" + d.id) .attr("stroke", "white") .style("fill", colormode) .attr("r", (d) => centralitySize(d[centrality_value])); // Apply the general update pattern. // exit const linkExit = link.exit().remove(); const nodeExit = node.exit().transition(end_t).remove(); nodeExit .selectAll("circle") .style("fill", "lightgrey") .transition(end_t) .attr("r", 0) .transition(end_t); // update const linkU = link .selectAll("line") .data(links, (d) => d.source.id + ":" + d.target.id); node.raise(); const nodeCircU = node.selectAll("circle").data(nodes, (d) => d.id); nodeCircU .transition(t) .style("fill", colormode) .attr("r", (d) => centralitySize(d[centrality_value])); node.raise(); // Update and restart the simulation. simulation.nodes(nodes); simulation.force("link").links(links); simulation .force( "collide", d3.forceCollide().radius((d) => centralitySize(d[centrality_value]) + 2) ) .on("tick", ticked); function ticked() { nodeCirc.attr("cx", (d) => d.x).attr("cy", (d) => d.y); nodeCircU.attr("cx", (d) => d.x).attr("cy", (d) => d.y); linkU .attr("x1", (d) => d.source.x) .attr("y1", (d) => d.source.y) .attr("x2", (d) => d.target.x) .attr("y2", (d) => d.target.y); linkLine .attr("x1", (d) => d.source.x) .attr("y1", (d) => d.source.y) .attr("x2", (d) => d.target.x) .attr("y2", (d) => d.target.y); node.raise(); } //zoom svg.call(d3.zoom().on("zoom", zoomed)); function zoomed(event) { g.attr("transform", event.transform); } svg.call( d3.zoom().transform, d3.zoomIdentity.translate(width / 2, height / 2) ); return svg.node(); }37:T518,This paucity of women within the SHEAR network is also reflected in measures of network influence. **Betweenness** is a calculation of influence which measures how often an individual is on the shortest path between any two other members of the network. If you know Alice, Bob, and Cate, but only Cate knows Dan, Emily, and Fatima, then Cate has the highest betweenness because she knows people who don't otherwise know each other. In the SHEAR network, the gender of these influential scholars reached a tipping point in 2015. Prior to 2015, the majority of influential individuals in the SHEAR network were all male, often by a large majority. These were the people who not only knew many people, but also played an important part in connecting the network because the people they were connected to knew many others. Since 2015, the majority of these influential scholars have been women, though women have not yet been such a large majority as men were in the early years of the network. Below, I'll discuss who these scholars were at a few important points in the life of the network, but if you'd like to see for yourself who was among the most influential for a given year, select a year in the network player above and check the table "Top individuals" just below the network visualization.38:T441,A few years back there was discussion on twitter about the relative openness of SHEAR to junior scholars, especially graduate students. I don't have access to any numbers related to panel acceptance, but looking at who has presented with who over the life of the network, there have been some major shifts in how much highly-connected scholars do (or do not) tend to cluster together on panels. The majority of participants at SHEAR are not highly connected. Eighteen hundred participants, or 60% of the almost three thousand people who have ever presented at SHEAR--have five or fewer connections, equal to presenting on only one panel. Ninety percent of all participants ever, like me, have 18 or fewer connections in the SHEAR network, equal to presenting on three or fewer panels. Only 8% of participants have 19 or more connections, with some major outliers like Alan Taylor and Rosemarie Zagarri, who have 101 and 99 connections respectively, and who are also the most influential members of the network by betweenness because they've been connected to so many people over time.39:T76f,At various points in the life of the conference, these highly connected individuals have tended to cluster together. **Degree segregation** is a calculated score on a scale of 1 to -1, where 1 indicates that highly connected individuals are only connected to other highly connected individuals and -1 indicates individuals with high and low numbers of connections mix equally with one another. SHEAR has had some large variance over its lifetime. [In my work on early American baptismal networks](https://observablehq.com/@mkane2/baptismal-network-comparison-statistics), the degree segregation in the SHEAR network is much more variable and extreme. (I find baptismal networks an interesting comparison because A. I've looked at a lot of them, B. they tend to be similar in size to the 3000 member SHEAR network, and C. each baptism of a child creates a micro-network of similar size to the micro-network created by a conference panel.) Early American baptismal networks tended to have relatively unchanging degree segregation between 0 and 0.2, while SHEAR's degree segregation has reached nearly 0.5 early in the life of the conference with some peaks in 1994 and 2011 where the network became more segregated than average. The baptismal networks I've seen with very high degree segregation tend to be ones with transient or unstable populations, where only a few individuals stay for the life of the network; in baptismal networks where the population is consistent, degree segregation tends to get lower over time. This suggests to me that SHEAR has had periods where a small number of long-haul participants have consistently appeared at the annual meeting during periods of greater turnover in the rest of the program. In 2022, we're going into a period of increasing degree segregation, although the period 2014-2020 was among the least degree-segregated over the life of the conference.3a:T64a,Network attenuation tends to correlate with degree segregation. A network is more attenuated when a larger number individuals relative to the total size of the network are bridges. In the network animation above, there's a **giant component**, which is the component of the network with the most connected individuals. There are also many smaller, unconnected sub-components. Some of these join or leave the giant component over time; others do not. Many of them are created by single panels where all of the participants have only presented at one SHEAR meeting. A bridge is an individual in the giant component whose removal would create a new, unconnected sub-component. In my work on baptismal networks, network attenuation tends to go up when degree segregation goes up. The SHEAR network mostly follows this pattern, with the exception of 1990: SHEAR had its lowest degree segregation *and* its highest attenuation. This suggests to me that 1990 was a historiographic shift in the field. Prior to 1990, in the early years of the conference, the SHEAR network was its most degree segregated. People who knew each other tended to stay clustered together, suggesting it was perhaps more difficult for newer or junior scholars to make connections. In 1988-1990, many more new scholars entered the field (or at least began presenting at SHEAR). These newer members were sharing panels with highly-connected scholars who had presented at SHEAR previously, but the new individuals generally did not have connections with each other, making the network more attenuated with many unconnected branches.3b:Tdb6,I hesitate to call 1990 a tipping point because it was so early in the life of the conference, only 9 years after the first conference in 1981, but looking at network metrics there does seem to have been a big change between 1990 and 1994. If 1990 had low degree segregation and high attenuation, 1994 had high degree segregation and slightly lower, but still high, attenuation. Early American baptismal networks tended to have network attenuation between 5-8%. SHEAR has consistently had attenuation over 8% for most of the life of the conference, with the exception of 2016-2022, when the network has been the most densely connected it's ever been. Looking at the most influential participants measured by their betweenness in 1990 and 1994, there seems to be a shift in the historiographic topics centered at the conference. In 1990, the top 5 participants by betweenness were Willard C. Klunder, John M. Belohlavek, C. Edward Skeen, Paul H. Bergeron and Deborah Van Broekhoven; historians of the Civil War, Jackson, Andrew Johnson, and the War of 1812. Deborah Van Broekhoven is something of an outlier in that list, being the only woman in the top 20 for that year as well as the most influential person in the network in 1994. In addition to Van Broekhoven, the 20 most influential scholars in 1994 included for the first time scholars of race, abolition and slavery, suggesting that the historiographic focus of the conference had begun to shift. This is because beginning in 1989 Van Broekhoven was the main connector between a densely interconnected subnetwork and the rest of the giant component. This dense subnetwork continued to grow and included mainly historians of women, slavery, the family, and religion like Carol Lasser, Edith Gelles, Lisa Norling, Jonathan Sassi, Neely Bruce, Constance B. Schultz, and others. This gender/slavery/religion cluster continued to grow until 1997, when the participants began to diffuse into the wider network--perhaps as these topics became more central to the profession. Around the same time, another cluster of participants whose work tends to focus on slavery and race, gender, and pedagogy started to grow, including scholars like Emily Conroy-Krutz, Cate Denial, James B. Stewart, Rosemarie Zagarri, and Kathleen DuVal; since 2016 this cluster has started to decline in size, suggesting that we are perhaps in the middle of another historiographic shift. These clusters are detected programmatically as **modularity classes**. A modularity class is a group that has denser connections between members of the same modularity class than with other members of the network. In some cases, like the Van Broekhaven gender/slavery/religion cluster (modularity class 27) between 1989 and 1997 or the race/gender/pedagogy cluster between 1997 and 2016 (modularity class 16), these modularity classes stand out very clearly from the rest of the network. Unlike many other network measures, I calculated the modularity classes with the whole lifetime network and not a 7 year window; this was to see if historiographic groupings were clearly divided over the whole life of the network. Some historiographic clusters, like class 16 and class 27, cluster clearly around historiographic interests and wax and wane in membership, others are less so. (Numbering of modularity classes don't matter, they're just arbitrary labels).3c:T506,The largest modularity class in the SHEAR network is class 11 with just over 500 members over the life of the network. This is the modularity class to which most SHEAR presidents and JER editors have belonged in recent years, along with modularity class 23. Up into the 1990s, class 23 and class 11 traded off as the community with the most influential members and the class of the JER editors and SHEAR presidents until about 2000, when class 11 edged out class 23. (Members of class 23 continue to participate at SHEAR, just they tend not to be editors and presidents now). Class 11 includes scholars like past and current JER editors Michael Morrrison and Johan Neem, past and current SHEAR presidents Joanne Freeman and Amy Greenburg, and many SHEAR committee members including Seth Cotlar, Stacey Robertson, and Gregory Nobles, among others. (Because class 11 is the largest, it also includes many other folks like Michael Hattem and Karl Jacoby who--I think?--have not served in any official SHEAR roles.)3d:T14fa,These modularity classes wax and wane over time, although they don't really have clear start and end points, and many members of the class stay in the network even if the class itself starts to grow smaller. Van Broekhoven's gender/slavery/religion class 27 still has members in the network in 2022, but many fewer and most of their connections are mostly with others now. Likewise, the gender/race/pedagogy class 16 mostly had connections between its own members circa 1997-2014, but this cluster has started to grow smaller. (NB regarding modularity classes: I don't think they're very good proxies for actual historiography, because they track groupings of scholars rather than groupings of ideas. People can change their areas of interest over time, and that may be reflected in the people they associate with at conferences, but I think it would have a lag of at least a couple years. My use of "scholars like X Y and Z who publish on A B and C" does a lot of work to flatten groups of 30-500 people with a lot of different interests for the sake of brevity. I'm not sure how well my own modularity class reflects my own scholarly interests: I'm in class 34, a relatively tiny class of 22 people which includes scholars like Mark Boonshaft, Jessica Parr, Lindsay Keiter and Elizabeth Kelly Gray--perhaps this class is interested in early national politics, gender, and digital methods? I consider myself a historian of women who uses digital methods with one foot in the 17th century, so ¯\\\_(ツ)_/¯) Other clusters started to emerge in 2000 and 2011. 2000 had low degree segregation and relatively low network attenuation, and two small classes that are still growing started to cohere. Class 15 includes scholars like Dan Richter, Christina Snyder, Nicole Eustace, Stephen Aron, Michael Witgen, and Nancy Shoemaker, mostly scholars of Indigenous histories. class 14 includes scholars like Nancy Hewitt, Vanessa Holden, Cameron Shriver, and Joseph Adelman, who work on rhetorics of race and political belonging. 2000 had relatively greater network cohesion with low degree segregation and low attenuation; in the network visualization you can see this with relatively dense connections throughout the network and no wide-flung branches. Unlike 1990, newer scholars coming into SHEAR were relatively well inter-mixed with more highly connected scholars. I don't necessarily think that classes 14 and 15 started to cohere as historiographic groups because of this shift in the wider network (for one, many of the scholars in these classes were publishing in their areas before 2000), but it's interesting to think about whether trends in network connections have any influence in how central scholarship on areas like African American citizenship or Indigenous sovereignty is perceived. Class 5 and class 20 started to emerge in 2011, when the network had high degree segregation and high attenuation. Class 5 includes scholars like Ed Baptist, Karin Wulf, Natalie Joy, and Jessica Choppin Roney, with interests in politics, capitalism, and abolition; class 20 includes Martha Jones, Kabria Baumgartner, Kate Masur, and Peter Koster who work on race and politics. 2011 had the highest attenuation and degree segregation since the 90s, suggesting that perhaps scholarship of the early republic was in a period of re-entrenchment of highly connected scholars in the center with integration of new, less well connected branches. In the 40 years of SHEAR, these times of high attenuation and high degree segregation seem like the points when new subfields come to the fore. We're going into another one of these periods right now in 2022, with degree segregation and network attenuation increasing from the historic low of 2019, when the network was the most densely interconnected it's ever been. The two major classes that seem related to work in gender history, class 16 and class 27, are declining in numbers and their members seem integrated into the wider network, perhaps reflecting the trend towards greater integration of women's and gender history in the broader field, even though women historians are more influential within the network than they've ever been. The top 5 most influential participants by betweenness this year are current SHEAR president Amy Greenberg, Emily Conroy-Krutz, Rosemarie Zagarri, Stacey Robertson, Johann Neem, who publish on US and the world, religion, gender, politics, slavery, and intellectual history, perhaps reflecting the turn towards global histories and continental approaches. Class 11, which has historically been the class of JER editors and SHEAR presidents, is currently declining in numbers like the previous JER editor/SHEAR president class 23 declined in the 1990s. Based on past trends in the SHEAR network, my guess is that in the next 5-10 years there might be another modularity class where the majority of JER editors and SHEAR presidents cluster, but I couldn't guess which one it will be.3e:T840,isMobile = { if ( /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|ipad|iris|kindle|Android|Silk|lge |maemo|midp|mmp|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i.test( navigator.userAgent ) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test( navigator.userAgent.substr(0, 4) ) ) { return true; } else { return false; } }3f:[] 32:{"id":"472964dcd26d1f7f","slug":"society-for-historians-of-the-early-american-republic-annu","title":"Society for Historians of the Early American Republic annual meeting network, 1981-2022","thumbnail":"b13cbac496bd9573bc7c7defddbe9f7ab33df3ecd6845df9197de03851c22c8d","access_level":"public","create_time":"$D2022-06-20T19:08:31.834Z","update_time":"$D2022-07-21T14:25:36.749Z","view_time":null,"first_public_time":"$D2022-06-20T20:44:53.802Z","has_unpublished_edits":null,"fork":{"id":"e43e2e14b19821f6","slug":"temporal-network-selection","title":"Baptismal Network Selection","owner":{"id":"aa9e7a8a29012c93","login":"mkane2","name":"Maeve Kane"}},"stars":5,"starred":false,"owner":{"type":"team","id":"aa9e7a8a29012c93","login":"mkane2","name":"Maeve Kane","avatar_url":"https://avatars-next.observableusercontent.com/10f27e44a96d5e36afe9f72cf1af8b5efa3b0f668929d6850e6e7d05867b630c","tier":"starter_2024"},"version":1819,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c33c0c6945323f32","login":"mkane2","name":"Maeve Kane","avatar_url":"https://avatars-next.observableusercontent.com/10f27e44a96d5e36afe9f72cf1af8b5efa3b0f668929d6850e6e7d05867b630c","tier":null}],"members":[{"type":"individual","id":"c33c0c6945323f32","login":"mkane2","name":"Maeve Kane","avatar_url":"https://avatars-next.observableusercontent.com/10f27e44a96d5e36afe9f72cf1af8b5efa3b0f668929d6850e6e7d05867b630c","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":3,"collection":null,"type":"notebook","body":{"title":"Society for Historians of the Early American Republic annual meeting network, 1981-2022","cells":[{"id":0,"value":"# Society for Historians of the Early American Republic annual meeting network, 1981-2022","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1361,"value":"About ${wordcount} words, ${formatTime(readTime)} read.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1480,"value":"Maeve Kane, University at Albany | mkane2@albany.edu | [@MaeveKane](https://twitter.com/MaeveKane)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1810,"value":"> Just here to vanity search? [Jump below](#cell-1186)\n> \n> Feel free to download/screencap images and/or retweet. [Twitter thread here](https://twitter.com/MaeveKane/status/1550121303293603842).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1379,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":682,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1003,"value":"viewof size_measure = Inputs.radio(\n new Map([\n [\"Number of connections\", \"degree\"],\n [\"Network influence\", \"between\"]\n ]),\n { value: \"degree\", label: \"Node size\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":996,"value":"viewof speed = Inputs.radio(\n new Map([\n [\"Slow\", 6000],\n [\"Fast\", 4000]\n ]),\n { value: 6000, label: \"Play speed\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":979,"value":"viewof time = Scrubber(times, {\n delay: speed,\n loop: true,\n autoplay: false,\n format: (date) =>\n date.toLocaleString(\"en\", {\n // month: \"long\",\n // day: \"numeric\",\n // hour: \"numeric\",\n year: \"numeric\",\n timeZone: \"UTC\"\n })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1768,"value":"

${format(time)}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":359,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"viewof colormode = Inputs.radio(\n new Map([\n [\"Community\", modcolor],\n [\"Gender\", gendercolor],\n [\"Bridges\", bridgecolor]\n ]),\n { value: modcolor, label: \"Color network by\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1453,"value":"Swatches(\n d3.scaleOrdinal(\n swatch_type,\n d3.quantize(d3.interpolateRainbow, swatch_type.length + 1)\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1199,"value":"### Top 20 individuals by ${size_measure} in ${format(time)}\n${nodes\n .sort(function (x, y) {\n return d3.descending(x[centrality_value], y[centrality_value]);\n })\n .slice(0, 20).filter((d) => d.gender == \"female\").length} women\n${nodes\n .sort(function (x, y) {\n return d3.descending(x[centrality_value], y[centrality_value]);\n })\n .slice(0, 20).filter((d) => d.gender == \"male\").length} men","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1144,"value":"viewof selected = Inputs.table(\n nodes\n .sort(function (x, y) {\n return d3.descending(x[centrality_value], y[centrality_value]);\n })\n .slice(0, 20),\n {\n columns: top_columns,\n sort: centrality_value,\n required: false,\n reverse: true,\n format: {\n start: (x) => format(x),\n end: (x) => format(x)\n }\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1488,"value":"Now that you've presumably played the network animation and thought to yourself *cool but so what*, here's some thoughts. The network had some major changes in 1990, 1994, 2000 and 2011. In 2022 the network looks very average considered within the lifespan of the network with the exception of gender composition. One of the things I found most shocking when I ran the numbers was how very few women there have historically been at SHEAR meetings, both in terms of participants and \"influential\" members of the network.\n\nFor the first 20 years of the conference, women made up barely 20% of participants in each 7-year window. Perhaps that shouldn't be shocking to me, but it is. This year, 2022, is the first year in *the entire forty-year life of the conference* that women make up 50% of participants in the previous seven years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1099,"value":"line_chart = LineChart(change, {\n x: (d) => parseTime(+d.year),\n y: (d) => d.network_pct / 2,\n yLabel: \"↑ Women as % of participants in previous 7 years\",\n yFormat: \"%\",\n width,\n height: sm_height,\n color: \"steelblue\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1499,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1502,"value":"gender_chart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1506,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1068,"value":"histochart = BarChart(histogram, {\n x: (d) => d.degree,\n y: (d) => d.count,\n xDomain: [...Array(33).keys()], // sort by descending frequency\n // yFormat: \"%\",\n yLabel: \"↑ # participants with total connections\",\n xLabel: \"Degree\",\n width,\n height: sm_height,\n color: \"steelblue\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1514,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1203,"value":"degree_chart = LineChart(change, {\n x: (d) => parseTime(+d.year),\n y: (d) => d.degree_assort,\n yLabel: \"↑ Degree segregation\",\n width,\n height: sm_height,\n color: \"steelblue\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1517,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1107,"value":"bridges_change = LineChart(change, {\n x: (d) => parseTime(+d.year),\n y: (d) => d.bridges,\n yLabel: \"↑ Bridges as % of network\",\n yFormat: \"%\",\n width,\n height: sm_height,\n color: \"steelblue\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1521,"value":"$3b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1026,"value":"barchart = BarChart(mod_class, {\n x: (d) => d.key,\n y: (d) => d.value,\n xDomain: d3.groupSort(\n mod_class,\n ([d]) => -d.value,\n (d) => d.key\n ), // sort by descending frequency\n // yFormat: \"%\",\n yLabel: \"↑ Modularity class members\",\n width,\n height: sm_height,\n color: \"steelblue\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1529,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1430,"value":"stream","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"streamswatches","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1534,"value":"$3d","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1186,"value":"## Want to find yourself in this network?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1647,"value":"Interested in seeing how you (or someone else) shows up in this network? Search a name in the sortable table below and you'll get a list of direct connections and other members of their modularity class. If you have only one individual listed, they'll show up in the network animation as a black dot slightly larger than they otherwise would be. You can, for example, watch Alan Taylor's peregrinations through the SHEAR network over the years as he moved from periphery in 1985 to center in about 1992.\n\n[Jump back to network](#chart)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1188,"value":"viewof search = Inputs.search(data.nodes, {\n placeholder: \"Names or dates\",\n label: \"Search\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1190,"value":"viewof rows = Inputs.table(search, {\n columns: table_columns,\n sort: size_measure,\n reverse: true,\n format: {\n start: (x) => format(x),\n end: (x) => format(x),\n modularity: (modularity) =>\n htl.html`${modularity}`\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1602,"value":"## Connections to ${rows.length == 1 ? `${rows[0].name}` : `searched individual`} \nNote: Several cells will display below until only one individual is searched above. Narrow it down by typing out the name further (case agnostic).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1599,"value":"#### ${connections.length} Direct connections to ${rows[0].name} | ${format_pct(pct_female_connections)} of direct connections are female | connected to ${connected_classes} other subcommunities","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1237,"value":"viewof cnx_table = Inputs.table(await connections, {\n columns: table_columns,\n sort: size_measure,\n reverse: true,\n format: {\n start: (x) => format(x),\n end: (x) => format(x),\n modularity: (modularity) =>\n htl.html`${modularity}`\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1283,"value":"#### ${comodularity.length} Individuals in modularity class ${rows[0].modularity} with ${rows[0].name} | ${format_pct(pct_female_comodularity)} of community are female","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1277,"value":"Inputs.table(comodularity, {\n columns: table_columns,\n sort: size_measure,\n reverse: true,\n format: {\n start: (x) => format(x),\n end: (x) => format(x),\n modularity: (modularity) =>\n htl.html`${modularity}`\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1183,"value":"# Utilities","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1064,"value":"## Modularity class helpers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1041,"value":"mod_class = Array.from(mod, function (item) {\n return { key: item[0], value: item[1] };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1032,"value":"mod = d3.rollup(\n data.nodes,\n (v) => v.length,\n (d) => d.modularity\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"md`## Size`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":212,"value":"centrality_value = size_measure + \"_\" + format(time)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"centralitySize = {\n if (size_measure == \"between\") {\n return d3.scaleSqrt().domain([0, sizemax]).range([2, 20]);\n } else {\n return d3.scaleSqrt().domain([1, sizemax]).range([2, 20]);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":466,"value":"sizemax = d3.max(data.nodes, (d) => d[size_measure])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1365,"value":"## Find connections","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1794,"value":"table_columns = {\n if (isMobile) {\n return [\"name\"];\n } else {\n return [\n \"name\",\n \"gender\",\n \"start\",\n \"end\",\n \"bridge\",\n \"degree\",\n \"between\",\n \"modularity\"\n ];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1802,"value":"top_columns = {\n if (isMobile) {\n return [\"name\", \"gender\"];\n } else {\n return [\n \"name\",\n \"gender\",\n \"bridge\",\n \"degree\",\n \"between\",\n centrality_value,\n \"modularity\"\n ];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1321,"value":"pct_female_connections = connections.filter((d) => d.gender == \"female\")\n .length /\n (connections.filter((d) => d.gender == \"female\").length +\n connections.filter((d) => d.gender == \"male\").length)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1327,"value":"pct_female_comodularity = comodularity.filter((d) => d.gender == \"female\")\n .length /\n (comodularity.filter((d) => d.gender == \"female\").length +\n comodularity.filter((d) => d.gender == \"male\").length)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1271,"value":"comodularity = {\n if (rows.length == 1) {\n var cnx = data.nodes.filter(\n (node) => node.modularity == rows[0].modularity\n );\n // var cnx = data.nodes.filter((node) => node.modularity == rows[0].modularity);\n // var targets = [...new Set(ts.map((d) => d.modularity))];\n // var ss = data.links.filter((link) => link.target == rows[0].id);\n // var sources = [...new Set(ss.map((d) => d.source))];\n // var all = targets;\n // for (let i in targets) {\n // var thisnode = data.nodes.filter((x) => x.modularity === targets[i]);\n // console.log(thisnode);\n // cnx.push(thisnode);\n // }\n return cnx;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1223,"value":"connections = {\n if (rows.length == 1) {\n var cnx = [];\n\n var ts = data.links.filter((link) => link.source == rows[0].id);\n var other_ts = data.links.filter((link) => link.source.id == rows[0].id);\n\n var targets = [...new Set(ts.map((d) => d.target))];\n var other_targets = [...new Set(other_ts.map((d) => d.target.id))];\n var all_targets = targets.concat(other_targets);\n\n var ss = data.links.filter((link) => +link.target === +rows[0].id);\n var other_ss = data.links.filter((link) => link.target.id == rows[0].id);\n\n var sources = [...new Set(ss.map((d) => d.source))];\n var other_sources = [...new Set(other_ss.map((d) => d.source.id))];\n var all_sources = sources.concat(other_sources);\n\n var all = all_targets.concat(all_sources);\n // console.log(all);\n for (let i in all) {\n var thisnode = data.nodes.filter((x) => x.id === all[i])[0];\n // console.log(thisnode);\n cnx.push(thisnode);\n }\n return cnx;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1371,"value":"connected_classes = [...new Set(connections.map((d) => d.modularity))].length -\n 1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"md`## Time`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"parseTime = d3.timeParse(\"%Y\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"format = d3.timeFormat(\"%Y\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"contains = ({ start, end }, time) => start - 7 <= time && time <= end","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"times = d3\n .scaleTime()\n .domain([\n d3.min(data.nodes, (d) => d.start),\n d3.max(data.nodes, (d) => d.end)\n ])\n .ticks(\n +format(d3.max(data.nodes, (d) => d.end)) -\n +format(d3.min(data.nodes, (d) => d.start))\n )\n .filter((time) => data.nodes.some((d) => contains(d, time)))\n\n// times = d3.scaleTime()\n// .domain([d3.min(data.nodes, d => d.start), d3.max(data.nodes, d => d.end)])\n// .ticks(1000)\n// .filter(time => data.nodes.some(d => contains(d, time)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1072,"value":"times_modified = times.slice(4, times.length)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":800,"value":"pct = d3.format(\"%\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"md `## Color`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1688,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1722,"value":"colors = d3.quantize(d3.interpolateRainbow, community_count + 1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1628,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":85,"value":"swatch_type = {\n if (colormode == modcolor) {\n return modularity_cats;\n } else if (colormode == gendercolor) {\n return genders;\n } else if (colormode == bridgecolor) {\n return bridge_types;\n } else {\n return modularity_cats;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"modularities = d3.map(data.nodes.sort(d3.ascending), (d) => +d.modularity)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"modularity_cats = [...new Set(modularities.sort(d3.ascending))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"community_count = modularity_cats.length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"modcolor = {\n const scale = d3.scaleOrdinal(\n modularity_cats,\n d3.quantize(d3.interpolateRainbow, community_count + 1)\n );\n const swatch_type = modularity_cats;\n return (d) => scale(d.modularity);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"genders = [...new Set(d3.map(data.nodes, (d) => d.gender))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"gendercolor = {\n const scale = d3.scaleOrdinal(d3.quantize(d3.interpolateRainbow, genders.length + 1));\n return (d) => scale(d.gender);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"bridge_types = [...new Set(d3.map(data.nodes, (d) => d.bridge))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"bridgecolor = {\n const scale = d3.scaleOrdinal(\n d3.quantize(d3.interpolateRainbow, bridge_types.length + 1)\n );\n return (d) => scale(d.bridge);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":813,"value":"denomination_color = {\n const scale = d3.scaleOrdinal(d3.quantize(d3.interpolateRainbow, 9));\n return (d) => scale(d.denomination);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":523,"value":"md`## Tooltip`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"tooltip = d3\n .select(\"body\")\n .append(\"div\")\n .style(\"position\", \"absolute\")\n .style(\"visibility\", \"hidden\")\n .style(\"background-color\", \"white\")\n .style(\"border-radius\", \"5px\")\n .style(\"box-shadow\", \"0 0 10px rgba(0,0,0,.25)\")\n .style(\"padding\", \"10px\")\n .style(\"line-height\", \"1.3\")\n .style(\"font\", \"0.75rem sans-serif\")\n .attr(\"class\", \"tooltip\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"tooltip_in = function (event, d) {\n var degree_time = \"degree\";\n return tooltip\n .html(\n \"

\" +\n d.name +\n \"

Gender: \" +\n d.gender +\n \"
Connections: \" +\n d.degree +\n \"
Bridge: \" +\n d.bridge +\n \"
Modularity: \" +\n d.modularity\n )\n .style(\"visibility\", \"visible\")\n .style(\"top\", event.pageY + \"px\")\n .style(\"left\", event.pageX + \"px\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":640,"value":"tooltip_out = function () {\n return tooltip.transition().duration(50).style(\"visibility\", \"hidden\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":534,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1094,"value":"change = d3.csv(\n \"https://raw.githubusercontent.com/mkane2/shear-network/main/shear_density.csv\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"bridges = data.nodes.filter((d) => d.bridge != \"false\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":363,"value":"nodes = data.nodes\n .filter((d) => contains(d, time))\n .filter((d) => d[\"degree_\" + format(time)] >= 1)\n // .sort(function (x, y) {\n // return d3.ascending(+x[\"modularity\"], +y[\"modularity\"]);\n // })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"links = data.links.filter((d) => contains(d, time))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"// data = d3.json(filename)\ndata = JSON.parse(await d3.text(filename), (key, value) =>\n key === \"start\" || key === \"end\" ? parseTime(value) : value\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"filename = \"https://raw.githubusercontent.com/mkane2/shear-network/main/shear_graph.json\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1061,"value":"histogram = d3.csv(\n \"https://raw.githubusercontent.com/mkane2/shear-network/main/shear_hist.csv\",\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1347,"value":"## Word Counter","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1349,"value":"makewordcount = {\n let a = [];\n let textselect = d3.selectAll(\"p\").each(function (d, i) {\n a.push(\n d3\n .select(this)\n .text()\n .split(/\\s+/g)\n .filter((w) => w).length\n );\n });\n return a;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1352,"value":"wordcount = d3.sum(makewordcount)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1354,"value":"readTime = wordcount / 265","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1358,"value":"function formatTime(minutes) {\n if (minutes < 1) {\n const seconds = Math.ceil(minutes * 60);\n return `${seconds} second`;\n }\n minutes = Math.ceil(minutes);\n return `${minutes} minute`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"md`## Utilities`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1791,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1338,"value":"sm_height = 200","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"height = 650","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1315,"value":"format_pct = d3.format(\".0%\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":361,"value":"simulation = {\n if (this) return this.nodes(nodes); //.alpha(1).restart();\n return d3\n .forceSimulation()\n .force(\"charge\", d3.forceManyBody())\n .force(\n \"link\",\n d3.forceLink().id((d) => d.id)\n )\n .force(\"x\", d3.forceX())\n .force(\"y\", d3.forceY())\n .alphaTarget(1);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"import { Legend, Swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":397,"value":"import { Table } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1021,"value":"import {BarChart} from \"@d3/bar-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1097,"value":"import {LineChart} from \"@d3/line-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1424,"value":"import {\n gender_chart,\n streamswatches,\n stream\n} from \"@mkane2/shear-communities-over-time\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":605,"value":"Network code based on @bryangingechen's [Force Update Pattern port](https://observablehq.com/@bryangingechen/force-update-pattern-in-observable) of [Harry Steven's](http://harryjstevens.com/) [block](https://bl.ocks.org/HarryStevens/bc938c8d45008d99faed47039fbe5d49) and @mbostock's [Temporal Force-Directed-Graph](https://observablehq.com/@d3/temporal-force-directed-graph)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1493,"value":"## Unused","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1396,"value":"// density_chart = LineChart(change, {\n// x: (d) => parseTime(+d.year),\n// y: (d) => d.density,\n// yLabel: \"↑ Density\",\n// width,\n// height: sm_height,\n// color: \"steelblue\"\n// })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1422,"value":"\n\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W3f","stdlib":"1","titling":"auto"}} 33:"$32:body"