2b:null 2c:["$","title",null,{"children":"Children on the Move: Gender and Parenting in the Liberation of Children From Slavery | Annabelle's Workspace | Observable"}] 31:T5a4, Under cover of darkness one night over two hundred years ago a man named Frank escaped enslavement and brought with him one of his youngest sons named John. John was about nine years old and according to descriptions had inherited many of his father's features. Their family was held in bondage in Westmoreland County, Virginia and Frank was believed to be taking John towards Fairfax county where he had a free brother named Rogers. It is unknown how many other children Frank left behind and if they remained in the care of their mother or other family members. Most tragically, it is unknown if Frank and John made it to safety. However, Frank's decision to take his young son with him, liberating him from a life of enslavement, exemplifies an under explored pattern in the historiography of American slavery and freedom seeking: that enslaved men played a profound role in the liberation of enslaved children. Historians have increasingly studied maternal bonds in enslaved communities and in doing so have uncovered incredibly powerful and important stories. However, paternal relationships among enslaved people, as well as the experiences of freedom seeking children more generally, have largely been ignored in the historiography. Stories like Frank's and those visible below indicate that more study is needed in this area. In this essay, I will begin to ask and answer these questions through data visualization and interpretation. 32:T215b, The history of chattel slavery is one that charts the dehumanization of African and African descended people by their captors on the North American continent. Separation of enslaved families was both a mechanism and an outgrowth of this dehumanization.Because family separation was such a central element of American slavery, historians have prioritized studying the enslaved family as the field shifted toward cultural history. Focus turned to the complicated lived experiences of enslaved people as they existed within and beyond their captivity. Fugitivity is as important to historians as the conditions freedom seekers were fleeing. Prioritizing flight from bondage and various forms of resistance, historians have centered the agency of enslaved people, shedding light not only on the terror they were subjected to, but the ways in which they protected each other and fought their oppression at every juncture. Historians have also investigated the gendered elements of enslavement, as part of and separate from the study of enslaved families. Enslaved men escaped at much higher rates than enslaved women. This is partly due to the gendered division of forced labor and also due to family separation. The tasks that enslaved men were forced to do were more likely to take them away from the plantation, giving them more access to geographical knowledge and a broader network of people on which to rely.[1] Fathers, brothers, and sons were more often separated from their younger and female relatives than vice versa. This is because especially in agricultural settings, where the concentration of enslaved people was the highest, enslaved boys and younger-to-middle-aged men were bought and sold at the highest prices on account of their higher physical capacity for manual labor. [2] As a result of this trend of separation through sale, historians have often argued that enslaved boys and men were more likely to escape because they were not beholden to take care of their children or families like enslaved women, who were more often allowed to remain with their children. While this was surely true for many, it is not a complete truth. More recent historians have begun to interrogate this assumption and have found that, having been displaced from their families, these boys and men often absconded to reunite with them. Further, enslaved boys and men also often fled enslavement while still living with their families. They did so for many reasons, not the least of which being that they could return to free their loved ones either by stealing them away or purchasing their freedom. Historians have also corrected this earlier interpretation by looking at enslaved women. Scholars have argued that duty to their children actually encouraged some enslaved mothers’ flights. Further, historians have highlighted the many often ignored ways in which women resisted their enslavement including truancy, sabotage, controlled reproduction, and community building.[3] Community building became an important lens through which to see enslaved women’s agency as historians have begun writing about enslaved relationships with the language of kinship. Co-opting terminology used by anthropologists, ethnographers, and psychologists, historians have begun to analyze the kinship networks, “fictive kin”, and kinfolk relationships of enslaved people, but almost exclusively of enslaved women.[4] Both in the presence and absence of men, historians have found that relationship building beyond the biological family unit was essential to the survival of enslaved women and a vital strategy they employed to keep their families safe. This historiographical focus on the kinship networks of enslaved women has challenged historians to expand their definitions of maternity. Once again taking an interdisciplinary approach, historians in recent years have incorporated the contemporary work of psychologists and sociologists to describe the networks built by enslaved mothers as “othermothering” or other similar terms including “shared mothering,” “shared nurturing,” and “manymothering.” [5] Taking this approach, historians have proven that biology was not necessary for maternal connections to form among enslaved women and children. Women supported one other and took children or younger women they were not related to by blood or marriage under their wings. They did this in the form of shared childcare and other more quotidian duties, but also in more dangerous or dramatic ways. In flight from enslavement, maternal bonds were not always biological. Enslaved men have largely been left out of these studies on kinship and networks of support surrounding enslaved children. This is for a multitude of reasons. First, because of the aforementioned patterns of family separation through sale. Additionally, this scholarship has been overlooked due to ideas of gender roles, biological essentialism, and maternity that may have existed in enslaved communities but certainly persist in our popular understanding today. Masculinity is not broadly associated with emotion or care for children. When that notion is coupled with racist stereotypes of Black enslaved people, that they were subhuman, and racist stereotypes surrounding Black men specifically, that they were even more severely animalistic and threatening than Black women, the viewfinder through which to glimpse enslaved fatherhood becomes clouded. Historians have intervened in this scholarly absence, revealing the ways in which enslaved fathers have been underrepresented in the literature. One of the most significant examples of this is Libra R. Hilde’s *Slavery, Fatherhood, and Paternal Duty in African American Communities over the Long Nineteenth Century*. Based primarily on autobiographies of formerly enslaved people and testimony from the Works Progress Administration’s 1930s interviews, Hilde talks about constraints on enslaved fatherhood, various forms of caretaking including the supply of material, educational, and emotional provisions. Above all, Hilde finds that the desire to provide liberty for their children was the greatest and yet most difficult to execute.[6] This book advanced the field with its sweeping and qualitative analysis of enslaved fatherhood. The chapter on escape and purchase provides insight into the deeply emotional motivations behind self emancipation and the calculations that had to be made by enslaved fathers as they weighed whether to abscond on their own or make the much riskier choice to try and save their families. What this study leaves unsaid is in part due to sourcing. Qualitative sources can only provide so much in the way of assessing trends. How often did enslaved fathers make the riskier choice to escape with their children? Did they do so more or less than enslaved mothers? Additionally, by relying on the recollections of children about their fathers, the subjects of study are inherently limited to a more “conventional” definition of family relationships. Historians have yet to ask questions of otherfathering among enslaved men, particularly within fugitivity. That is, did enslaved fathers take on paternal roles with enslaved children they may not have been related to and take their roles so far as to liberate those children from bondage? By looking at what we know of these most frightening moments of enslaved men’s lives, how can extra-“family” paternal relationships also be made visible? Finally, how can digital methods return some humanity to enslaved father figures, biological or otherwise? ## Visualizing Communities of Care In accordance with my research questions, I have selected the Freedom on the Move digital archive as my dataset. I filtered the project's more than 32 thousand “runaway ads” down to only those that describe groups comprised of at least one child (122 groups total). I defined child as any self-emancipating person whose approximate age is marked either as less than 18 years old or as a more qualitative estimate of “child,” “infant,” “boy,” or “girl.” My categorization of adults followed the opposite pattern: anyone whose approximate age is marked either as 18 years or older or as “adult,” “man,” “woman,” “elderly,” or “old.” Some have no age approximation and thus are labeled as age “unknown.” Next, the gender categories visible in the ads, and thus represented in the data, are confined to three: “male,” “female,” and “unknown.” 33:T43a2,viewof network = { const width = 900, height = 650; const escapeYear = escapeYearSlider; const groupSize = groupSizeSlider; const container = html`

`; // === 2. AGE CATEGORY NORMALIZER (FIXED DECIMAL REGEX) === const isChild = val => { if (!val) return false; const lc = String(val).toLowerCase().trim(); // Check for age-related keywords const keywords = ["child", "infant", "boy", "girl", "baby", "youth", "minor"]; if (keywords.some(k => lc.includes(k))) return true; // Extract numbers, including decimals without leading digit (e.g., ".16") const match = lc.match(/(\d*\.\d+|\d+)/); if (match) { const num = parseFloat(match[0]); return num < 18; } return false; }; const isAdult = val => { if (!val) return true; const lc = String(val).toLowerCase().trim(); // Check for age-related keywords const keywords = ["adult", "elderly", "man", "woman", "old", "senior"]; if (keywords.some(k => lc.includes(k))) return true; // Extract numbers, including decimals without leading digit const match = lc.match(/(\d*\.\d+|\d+)/); if (match) { const num = parseFloat(match[0]); return num >= 18; } return true; // Default to adult if no age info }; // === 3. FILTERED DATASETS === const nodesFilteredByYear = nodes.filter(d => d.escape_year <= escapeYear); // Set age and gender categories for each node nodesFilteredByYear.forEach(node => { node.isChild = isChild(node.approximate_age); node.isAdult = isAdult(node.approximate_age); node.isMale = (node.gender || "").toLowerCase() === "male"; node.isFemale = (node.gender || "").toLowerCase() === "female"; // Create a category field for easier reference if (node.isChild && node.isMale) node.category = "childMale"; else if (node.isChild && node.isFemale) node.category = "childFemale"; else if (node.isAdult && node.isMale) node.category = "adultMale"; else if (node.isAdult && node.isFemale) node.category = "adultFemale"; else if (node.isChild) node.category = "childUnknown"; else node.category = "adultUnknown"; }); const nodeIDs = new Set(nodesFilteredByYear.map(d => String(d.gewc_runaway_id).trim())); const links = gewc_edges1 .map(d => ({ source: String(d.source_id).trim(), target: String(d.target_id).trim() })) .filter(d => nodeIDs.has(d.source) && nodeIDs.has(d.target)); function detectCommunities(nodes, links) { const communities = new Map(); let communityId = 0; const graph = new Map(); nodes.forEach(node => graph.set(String(node.gewc_runaway_id).trim(), [])); links.forEach(link => { graph.get(link.source).push(link.target); graph.get(link.target).push(link.source); }); const visited = new Set(); function bfs(startNode) { const queue = [startNode], community = []; visited.add(startNode); while (queue.length > 0) { const node = queue.shift(); community.push(node); for (const neighbor of graph.get(node) || []) { if (!visited.has(neighbor)) { visited.add(neighbor); queue.push(neighbor); } } } return community; } for (const nodeId of graph.keys()) { if (!visited.has(nodeId)) { const community = bfs(nodeId); community.forEach(id => communities.set(id, communityId)); communityId++; } } return communities; } const communities = detectCommunities(nodesFilteredByYear, links); nodesFilteredByYear.forEach(node => { node.community = communities.get(String(node.gewc_runaway_id).trim()); }); const communityCounts = {}; nodesFilteredByYear.forEach(n => { const cid = n.community; communityCounts[cid] = (communityCounts[cid] || 0) + 1; }); nodesFilteredByYear.forEach(n => { n.communitySize = communityCounts[n.community] || 0; }); const nodesFiltered = nodesFilteredByYear.filter(n => communityCounts[n.community] >= groupSize ); const validNodeIDs = new Set(nodesFiltered.map(n => String(n.gewc_runaway_id).trim())); const filteredLinks = links.filter(l => validNodeIDs.has(l.source) && validNodeIDs.has(l.target) ); // New color function based on four categories const color = node => { if (node.isChild && node.isMale) return "#a6cee3"; // light blue for child males else if (node.isChild && node.isFemale) return "#fb9a99"; // pink for child females else if (node.isAdult && node.isMale) return "#1f78b4"; // blue for adult males else if (node.isAdult && node.isFemale) return "#e31a1c"; // red for adult females else if (node.isChild) return "#aba7a7"; // light gray for unknown gender children return "#424242"; // Gray for unknown gender adults }; // Track expanded clusters const expandedClusters = new Set(); // Track strength multipliers for different forces let chargeStrength = -30; let linkDistance = 20; let clusterStrength = 0.3; const tooltip = html``; document.body.appendChild(tooltip); const svg = d3.select(DOM.svg(width, height)) .attr("viewBox", [0, 0, width, height]) .style("border", "1px solid #ccc"); const zoom = d3.zoom() .scaleExtent([0.1, 10]) .on("zoom", (event) => g.attr("transform", event.transform)); svg.call(zoom); const g = svg.append("g"); // Add a transparent background to capture clicks for deselection g.append("rect") .attr("width", width) .attr("height", height) .attr("fill", "transparent") .on("click", () => { // Deselect all clusters expandedClusters.clear(); updateHighlighting(); updateSimulationForces(); }); const link = g.append("g") .attr("stroke", "#aaa") .attr("stroke-opacity", 0.6) .selectAll("line") .data(filteredLinks) .join("line") .attr("stroke-width", 0.5); const node = g.append("g") .selectAll("circle") .data(nodesFiltered) .join("circle") .attr("r", d => { const baseSize = d.isChild ? 5 : 4; const connections = filteredLinks.filter(l => l.source === String(d.gewc_runaway_id).trim() || l.target === String(d.gewc_runaway_id).trim() ).length; return baseSize + (connections * 0.5); }) .attr("fill", color) .attr("stroke", "#fff") .attr("stroke-width", 1.5) .attr("class", d => `community-${d.community}`) .on("mouseover", function(event, d) { const id = String(d.gewc_runaway_id).trim(); const connectedIds = new Set(); filteredLinks.forEach(l => { if (l.source === id) connectedIds.add(l.target); if (l.target === id) connectedIds.add(l.source); }); node.attr("opacity", n => String(n.gewc_runaway_id).trim() === id || connectedIds.has(String(n.gewc_runaway_id).trim()) ? 1 : 0.2 ); link.attr("opacity", l => l.source === id || l.target === id ? 1 : 0.1 ); const communityMembers = nodesFiltered.filter(n => n.community === d.community); const childrenInGroup = communityMembers.filter(n => n.isChild).length; const skillsClean = (d.skills || "").toLowerCase(); const showSkills = !(["undefined_value", "not_listed", "unknown"].includes(skillsClean)); tooltip.style.display = "block"; tooltip.innerHTML = ` ${d.fullname || "Unknown"}
Gender: ${d.gender || "Unknown"}
Age: ${d.approximate_age || "Unknown"}
${showSkills ? `Skills: ${d.skills}
` : ""} Escape Year: ${d.escape_year || "Unknown"}
Group Size: ${communityMembers.length} people
Children in Group: ${childrenInGroup}
Click to expand this group `; tooltip.querySelector(".expand-cluster").onclick = (e) => { e.stopPropagation(); expandCluster(d.community); }; tooltip.style.left = (event.pageX + 10) + "px"; tooltip.style.top = (event.pageY + 10) + "px"; }) .on("mousemove", event => { tooltip.style.left = (event.pageX + 10) + "px"; tooltip.style.top = (event.pageY + 10) + "px"; }) .on("mouseout", () => { updateHighlighting(); tooltip.style.display = "none"; }) .on("click", (event, d) => { event.stopPropagation(); expandCluster(d.community); }); function expandCluster(communityId) { if (expandedClusters.has(communityId)) { expandedClusters.delete(communityId); } else { expandedClusters.add(communityId); } updateHighlighting(); updateSimulationForces(); // Focus on the expanded cluster if (expandedClusters.size === 1) { const clusterNodes = nodesFiltered.filter(n => n.community === communityId); const x = d3.mean(clusterNodes, d => d.x); const y = d3.mean(clusterNodes, d => d.y); const scale = 3; // Increase zoom level for better visibility svg.transition() .duration(750) .call(zoom.transform, d3.zoomIdentity .translate(width/2, height/2) .scale(scale) .translate(-x, -y) ); } } function updateHighlighting() { if (expandedClusters.size === 0) { // No clusters selected, show all node.attr("opacity", 1); link.attr("opacity", 0.6); } else { // Highlight selected clusters node.attr("opacity", d => expandedClusters.has(d.community) ? 1 : 0.1); link.attr("opacity", l => { const sourceComm = l.source.community; const targetComm = l.target.community; return expandedClusters.has(sourceComm) && expandedClusters.has(targetComm) ? 1 : expandedClusters.has(sourceComm) || expandedClusters.has(targetComm) ? 0.3 : 0.1; }); } } function updateSimulationForces() { // Update charge force based on expanded clusters const chargeForce = d3.forceManyBody() .strength(d => { if (expandedClusters.size === 0) return chargeStrength; return expandedClusters.has(d.community) ? chargeStrength * 3 : chargeStrength; }); // Update link distance based on expanded clusters const linkForce = d3.forceLink(filteredLinks) .id(d => String(d.gewc_runaway_id).trim()) .distance(l => { const sourceComm = l.source.community; const targetComm = l.target.community; if (expandedClusters.size === 0) return linkDistance; if (expandedClusters.has(sourceComm) && expandedClusters.has(targetComm)) { return linkDistance * 2; // Increase distance between nodes in expanded clusters } return linkDistance; }); // Update or replace forces simulation .force("link", linkForce) .force("charge", chargeForce) .alpha(0.3) .restart(); } const simulation = d3.forceSimulation(nodesFiltered) .force("link", d3.forceLink(filteredLinks).id(d => String(d.gewc_runaway_id).trim()).distance(linkDistance)) .force("charge", d3.forceManyBody().strength(chargeStrength)) .force("center", d3.forceCenter(width / 2, height / 2)) .force("x", d3.forceX(width / 2).strength(0.01)) .force("y", d3.forceY(height / 2).strength(0.01)) .force("cluster", alpha => { // Adjust cluster force based on whether clusters are expanded for (const d of nodesFiltered) { if (expandedClusters.has(d.community)) continue; // Skip cluster force for expanded clusters let cx = 0, cy = 0, count = 0; for (const d2 of nodesFiltered) { if (d.community === d2.community) { cx += d2.x; cy += d2.y; count++; } } if (count > 0) { d.vx += (cx / count - d.x) * alpha * clusterStrength; d.vy += (cy / count - d.y) * alpha * clusterStrength; } } }) .alphaDecay(0.01) .on("tick", () => { link.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.attr("cx", d => d.x).attr("cy", d => d.y); }); node.call( d3.drag() .on("start", function(event, d) { if (!event.active) simulation.alphaTarget(0.3).restart(); d.fx = d.x; d.fy = d.y; }) .on("drag", function(event, d) { d.fx = event.x; d.fy = event.y; }) .on("end", function(event, d) { if (!event.active) simulation.alphaTarget(0); d.fx = null; d.fy = null; }) ); // Create controls container for better organization const controls = html`

`; const restartButton = html``; restartButton.onclick = () => simulation.alpha(1).restart(); const fitButton = html``; fitButton.onclick = () => { expandedClusters.clear(); updateHighlighting(); updateSimulationForces(); const bounds = svg.node().getBBox(); const parent = svg.node().parentElement; const scale = 0.9 / Math.max(bounds.width / parent.clientWidth, bounds.height / parent.clientHeight); const translate = [ parent.clientWidth / 2 - scale * (bounds.x + bounds.width / 2), parent.clientHeight / 2 - scale * (bounds.y + bounds.height / 2) ]; svg.transition() .duration(500) .call(zoom.transform, d3.zoomIdentity.translate(...translate).scale(scale)); }; const spreadButton = html``; spreadButton.onclick = () => { if (chargeStrength === -30) { // Spread mode chargeStrength = -120; linkDistance = 40; clusterStrength = 0.1; spreadButton.textContent = "Compress Clusters"; } else { // Default mode chargeStrength = -30; linkDistance = 20; clusterStrength = 0.3; spreadButton.textContent = "Spread All Clusters"; } updateSimulationForces(); simulation.alpha(1).restart(); }; // Add instruction text const instructions = html`

Tip: Click on any node to expand its cluster. Click background to reset.

`; // Add buttons to controls controls.append(restartButton, fitButton, spreadButton); // Updated legend for four categories const legend = html`

Legend:

● Adult Male

● Adult Female

● Child Male

● Child Female

● Adult (Unknown Gender)

● Child (Unknown Gender)

`; // Calculate statistics for each category const totalChildren = nodesFiltered.filter(n => n.isChild).length; const adultMales = nodesFiltered.filter(n => n.isAdult && n.isMale).length; const adultFemales = nodesFiltered.filter(n => n.isAdult && n.isFemale).length; const childMales = nodesFiltered.filter(n => n.isChild && n.isMale).length; const childFemales = nodesFiltered.filter(n => n.isChild && n.isFemale).length; const unknownGender = nodesFiltered.filter(n => !n.isMale && !n.isFemale).length; const totalGroups = Object.keys(communityCounts).filter(key => nodesFiltered.some(n => n.community === parseInt(key)) ).length; // Updated stats to include category counts const stats = html`

Network Stats:
Total People: ${nodesFiltered.length}

Adult Males: ${adultMales} (${Math.round((adultMales / nodesFiltered.length) * 100) || 0}%)
Adult Females: ${adultFemales} (${Math.round((adultFemales / nodesFiltered.length) * 100) || 0}%)
Child Males: ${childMales} (${Math.round((childMales / nodesFiltered.length) * 100) || 0}%)
Child Females: ${childFemales} (${Math.round((childFemales / nodesFiltered.length) * 100) || 0}%)
Unknown Gender: ${unknownGender} (${Math.round((unknownGender / nodesFiltered.length) * 100) || 0}%)

Total Children: ${totalChildren} (${Math.round((totalChildren / nodesFiltered.length) * 100) || 0}%)
Total Groups: ${totalGroups}

`; container.append(svg.node(), controls, instructions, legend, stats); return Object.assign(container, { value: container, dispose() { if (tooltip && tooltip.parentNode) tooltip.remove(); } }); }34:T8b4,This interactive network is the fist step in investigating the experience of freedom seeking children. It reconstructs the groups who enabled their flights. Aesthetically, the visualization highlights the presence of children in each of them and visually making plain that all gender categories are represented among the adults and children comprising the groups. This visualization also shows that there were very few large groups. Indicating that safety was not always in numbers. Interaction with the network also reveals that some of these groups are composed only of children. [give example?] It is likely that these groups were adolescents or older children escaping together, although this is not guaranteed and would need further inspection. The number of groups illustrated here is also significant. The fact that there are only 122 groups that saved children, out of over 32 thousands fugitive events, further affirms how the risk and danger of escape was multiplied exponentially when children were included. With its interactive features, this network visualization design allows deeper contact with the humanity of the people discussed in this study, rather than solely casting them as numbers. This reminds us that computational methods do not necessitate dehumanization. DH historically has also not required humanization. It is especially important when studying people who risked their lives to claim their freedom that we be intentional in not removing it from them once again. By making their names and other details visible, this visualization bridges the gap between data manipulation and nuanced tales of people in the past. Overall, the network communicates that these groups existed and provides a qualitative sense of their diversity, specifically, that it was not only enslaved women who saved children from bondage. This is an important finding and begins to fill the scholarly gap on enslaved men and children, but is not itself surprising. Moreover, broader trends across the 122 groups are not made visible here in this network visualization. Computing the demographic makeup of the groups brings the data closer to answering more distinct questions of childhood, kinship, and otherfathering. 35:T424,Plot.plot({ width: 625, title: "Frequency of Group Types", caption: "Figure 2. Three primary group types were derived from the data based on the gender breakdown of the adults. There were 0 groups where all of the adults have 'unknown' gender, so that bar space was removed. 'Mixed Gender' represents groups where there is at least 2 different gender markers among the adults. There were 5 groups of only children that do not appear in this visualization.", marginBottom: 50, x: { domain: ["Only Male Adults", "Only Female Adults", "Mixed Gender"], label: "Group Type" // ← this sets the x-axis label }, marks: [ Plot.barY( group_type_count_viz .filter(d => ["men", "women", "mixed"].includes(d.group_type)) .map(d => ({ label: d.group_type === "men" ? "Only Male Adults" : d.group_type === "women" ? "Only Female Adults" : "Mixed Gender", count: +d.count })), { x: "label", y: "count", tip: true } ) ] }) 36:T409,
Answering the question of _who_ saved enslaved children also gives a data-driven perspective on the measure of importance enslaved men held in the liberation of children. Figure [2] shows that the greatest number of groups (78) were composed of adults of mixed genders. Groups with only male adults were the second most common at 20. Finally, groups with only female adults account for 19 of the total 122. Taken together, these values show that children were most likely to escape in groups of mixed gender adults. However, the numbers of children saved by exclusively male adult and exclusively female adult groups are both significant and comparable. This data therefore suggests that enslaved men played a large role; that in their communities, despite family separation and other factors, paternal connection was highly valued and a tool of liberation for the youngest among them. Inquiring about the ages of children saved by each of these group types may provide more information about the nature of these relationships.37:Tf0d,This bar chart shows the age distribution of children saved in groups where all of the adults were labeled "male." This could mean a single man saving one or multiple children, or multiple men saving one or multiple children. "17" appears as second most common in the children's age distribution. Compared to the age distributions of the other two group types, this suggests that 17 year olds may have been more likely to escape in groups of exclusively male adults. In addition to instances where these adolescents were escaping with father figures, these runaway events may represent cases when 18 year old males or men in their early 20s escaped with their slightly younger peers. Though perhaps not fatherhood relationships, these cases, should they exist, do deserve be counted, as choosing to flee bondage with anyone younger was a risk that the older enslaved men should be acknowledged for taking. Further, I am choosing to protect the child label of adolescents so as not to perpetuate the tendency to “adultify” Black children. The approximate age of "child" is the most common in this distribution and the third most common is "<2", suggesting that, more than anything, these enslaved men were saving young people who could not have absconded on their own. They were not running only or even primarily with older children and adolescents. Without the presence of women who could have been these children's mothers, one is left to wonder about the relationships between these men and kids. It is possible that some of them were single fathers whose wives had died or been sold away. It is unlikely that this scenario accounts for every one of these runaway events. Alternatively, the children may have been orphaned by either natural causes or by sale and these enslaved men may have stepped in to take care of them, ultimately attempting to free them from a life of servitude. Some of these men may also have been chosen as father figures, trusted by enslaved mothers to take care of their children if they themselves could not flee. Overall, this data from Freedom on the Move has provided a window into what freedom seeking may have been like for children, on both a macro and a micro scale. Network analysis first brought these children's experiences into focus, allowing them to be seen in name and relation. As childhood, gender, parenting, and slavery were so inextricably connected, the data of children's escapes from bondage also provides an opportunity for digital methods to engage with historiographical debates in these areas. As found here, children were most often, though not always, saved by smaller groups of adults. In terms of gender, this data has affirmed scholarship on enslaved women, reflecting in powerful ways the arguments that enslaved women were intensely important in the lives and liberation of children as mothers and othermothers. Perhaps the most significant gender finding is derived from the final computations involving adult men who saved enslaved children. The sheer number of groups composed solely of men and children confirm that "shared nurturing" was not an exclusively feminine or maternal practice in enslaved communities. Rather, enslaved men voluntarily chose to emancipate children both on their own and as part of groups that may or may not have been considered family by a modern definition. This data strongly suggests that they did so at comparable rates to enslaved women. Family was about more than biology, and like what others have found to be true for enslaved maternity, paternal love was based on community, selflessness, and caring deeply for little ones. It emphasized lineage of a different kind entirely. These enslaved men, these fathers and otherfathers, hoped that the children they cared for would inherit freedom and a life they themselves were unable to live. 38:T129e,# Postscript and AI Statement This note aims to add an important caveat to the findings and interpretations above. While Freedom on the Move currently includes over 32,000 "runaway ads" and is the largest most comprehensive database of its kind as it stands, it is not complete nor is it entirely foolproof. The project team hopes to expand the database to eventually include what they estimate to be a total of 100,000-200,000 "runaway ads" that were published across the North American continent in the 18th and 19th centuries. Additionally, their transcript and metadata creation is done through crowdsourcing. This practice is valuable for a number of reasons and understandable especially given our current and former funding landscape, but also carries with it a greater potential for error. Some freedom seekers may have incorrect or unnecessarily blank fields attached to them. I currently have no reason to doubt my findings and further do not believe that clerical errors would meaningfully alter these trends. However, I am acknowledging the possibility that with further time to clean and recheck all of the metadata, the computational outputs could end up being different. Not yet knowing any one coding language myself, I did use generative AI and got help with these more technical components of the project. I will outline this help here. Having downloaded the massive csv files from Freedom On the Move, my professor Dr. Lincoln Mullen assisted me by running a script that created smaller sample sized versions of each table. I then uploaded these files to Claud AI and asked it to create an SQLite schema to connect them all. With that schema, I manually imported the full dataset into DB Browser for SQLite where I would run most of my queries going forward. Dr. Mullen also walked me through a test query (analyzing the gender breakdown of enslavers across the entire database). From here, I performed weeks worth of exploratory visualizations for which I only used generative AI when my queries produced error messages. These exploratory visualizations helped me hone in on my actual interests and questions. I then turned to network analysis. Once I myself filtered the database down to only children who ran in groups using SQLite, I created nodes and edges tables for to build the group network. When I put it in Observable as a ForceGraph, the network was bland, basic, and monochrome, so I turned to Claude AI and ChatGPT intermittently to visually enhance it and increase its interactivity, both of which serve my argument. Finding the derivative information about these groups was the most difficult part. I first asked the generative AI platforms to write me SQLite queries to create and investigate the gendered group types I was interested in. These queries gave me data that ended up reflecting the true trends, but the numbers were incredibly inflated. For example, with these original queries, I found that there were 83 groups of exclusively male adults and 53 groups of exclusively female adults, and 15 groups with mixed gender adults. These values added up to well over the 122 total groups. When I pointed out this discrepancy to ChatGPT, instead of creating new queries like I asked, the program queried my nodes and edges table and calculated these group type totals for me, but using Python. For example, ChatGPT counted only 18 exclusively male adult groups. I ran query after query in SQLite trying and failing to either replicate that number or get any results that actually added up to my true total. Finally, I figured it might be useful to use Python since that seemed to work at least internally for ChatGPT. Additionally, in almost every instance where I used generative AI, it had made and admitted both small and large errors. I had caught many of them or asked questions that revealed them, but wondered if there was some sort of error or hallucination happening in this case too. I wanted to either replicate and prove or disprove with confidence those generative results. My partner, Jackson Artis, does a lot of coding work in his profession and knows Python well. I gave him my nodes and edges tables as well as a few relevant tables from FOTM before asking him questions and telling him specifically what I wanted, querying him just as I had with the generative AI. The Python code he wrote and taught me is attached. Having been walked through and taught the steps and having category values distinct from both my original findings and that of ChatGPT that add up to my precise total, gives me confidence in this code and its results. Since the child:adult ratios and age difference investigations are derivative of these group types, I had to enlist my human AI to code and teach me those things as well.39:T687,# Notes [1] Stephanie M. H.Camp, Closer to Freedom: Enslaved Women and Everyday Resistance in the Plantation South. United States: University of North Carolina Press, 2005, 32.
[2] Jennifer L.Morgan, Laboring Women: Reproduction and Gender in New World Slavery. United States: University of Pennsylvania Press, Incorporated, 2011, 166
[3] Stephanie M. H.Camp, Closer to Freedom: Enslaved Women and Everyday Resistance in the Plantation South. United States: University of North Carolina Press, 2005.
[4] Jessica MarieJohnson, Wicked Flesh: Black Women, Intimacy, and Freedom in the Atlantic World. United States: University of Pennsylvania Press, Incorporated, 2020.
[5] “Othermothering: A Black Feminist History of Communal Early Childhood Education in America ”. 2024. Journal of African American Women and Girls in Education 4 (2): 108-27. https://doi.org/10.21423/jaawge-v4i2a112.; Bryant, Valerie. 2022. “Standing at the Water’s Edge: Manymothers in African American Culture.” Journal of Infant, Child, and Adolescent Psychotherapy 21 (2): 125–39. doi:10.1080/15289168.2022.2066916.;
[6] Libra R. Hilde, 'The God Part of Him: Slavery and Constraints on Fatherhood', Slavery, Fatherhood, and Paternal Duty in African American Communities over the Long Nineteenth Century (Chapel Hill, NC, 2020; online edn, North Carolina Scholarship Online, 23 Sept. 2021), https://doi.org/10.5149/northcarolina/9781469660677.003.0002, accessed 11 May 2025.
[7] Fergus M. Bordewich, Bound for Canaan: The Underground Railroad and the War for the Soul of America (New York, NY: HarperCollins, 2005), 353.3a:T7d4,imageData = [ { url: (await FileAttachment("FrankJohn.png").url()), caption: "Frank and John. Washington, D.C. August 23, 1803. Freedom on the Move." }, { url: (await FileAttachment("JackCarterBilly.png").url()), caption: "Jack and Carter's Billy. Washington, D.C. May 13, 1804. Freedom on the Move." }, { url: (await FileAttachment("JohnCinderellaFrank.png").url()), caption: "John, Cinderella, and Frank. Halifax, North Carolina. July 6, 1829. Freedom on the Move." }, { url: (await FileAttachment("JemmyAdamKeatingEleckOwenJOhnIsaacSuseyDollyChloeChildren.png").url()), caption: "Jemmy, Adam, Keating, Eleck, Owen, John, Isaac, Susey, Dolly, Chloe, and Children. Charleston, South Carolina. May 25, 1825. Freedom on the Move." }, { url: (await FileAttachment("HectorCarlos.png").url()), caption: "Hector and Carlos. Wilmington, North Carolina. December 28, 1773. Freedom on the Move." }, { url: (await FileAttachment("ByrdFields.png").url()), caption: "Byrd and Fields. Raleigh, North Carolina. October 14, 1845. Freedom on the Move" }, { url: (await FileAttachment("MaleChild.png").url()), caption: "4 year old boy. Charleston, South Carolina. September 6, 1795. Freedom on the Move." }, { url: (await FileAttachment("PhillWilliam.png").url()), caption: "Phill and William. New Orleans, Louisiana. April 3, 1844. Freedom on the Move." }, { url: (await FileAttachment("RomulusWillis.png").url()), caption: "Romulus and Willis. Raleigh, North Carolina. June 8, 1826. Freedom on the Move." }, { url: (await FileAttachment("WillCasey.png").url()), caption: "Will and Casey. Savannah, Chatham County, Georgia. September 12, 1781. Freedom on the Move." }, { url: (await FileAttachment("WillKittyLiddyChanceSabinaTomPopeJohnLucyVenusGranvilleMonteithMahomedyPhilipHneny.png").url()), caption: "Will, Kitty, Liddy, Chance, Sabina, Tom, Pope, John, Lucy, Venus, Granville, Monteith, Mahomedy, Philip, and Henry. Savannah, Chatham County, Georgia. May 11, 1785. Freedom on the Move." } ] 3b:T2bfb,### Python code import pandas as pd import numpy as np import matplotlib.pyplot as plt import os pd.options.mode.chained_assignment = None ## Public Variables csv_file = os.getcwd() + "/nodes_simplified.csv" collect_data_under_this_age_in_yrs = 1 ## If this number is set to 5, the data will have a column that sums every tally for people under ## 5 years old and the remaining data will look the same use_months_for_age_under_this_in_yrs = 2 ## If this number is set to 5, the column names will be in months instead of years for all ages up to ## but not including 5 years old ## Private Variables runaway_ad_header = "runaway_event_id" gender_header = "gender" age_header = "approximate_age" ## Functions def is_decimal(s): try: float(s) return True except ValueError: return False def is_number(s): return s.isnumeric() or is_decimal(s) def initialize_empty_data_frame_with_categories(categories): df = pd.DataFrame(columns=categories) elements = [] for cat in categories: elements.append(0) df.loc[len(df)] = elements return df def initialize_empty_data_frame_with_categories_and_indices(categories, index_list): df = pd.DataFrame(columns=categories, index=index_list) for idx in index_list: elements = [] for cat in categories: elements.append(0) df.loc[idx] = elements return df def return_column_from_dataframe(dataframe, column_name): return dataframe[column_name] def append_list_of_entries_to_list(list, entries): for entry in entries: list.append(str(entry)) return list def extend_list_with_list(list, new_list): list.extend(new_list) return list def return_sorted_numbers_in_list(list): numbers = [] for el in list: if is_number(el): numbers.append(float(el)) numbers.sort() return numbers def return_numbers_under_limit(numbers_list, limit): new_numbers = [] for num in numbers_list: if num < limit: new_numbers.append(num) return new_numbers def return_numbers_over_limit(numbers_list, limit): new_numbers = [] for num in numbers_list: if num > limit: new_numbers.append(num) return new_numbers def identify_ages_in_category(numbers_list, category): new_numbers = [] if category.lower() == "child": new_numbers = return_numbers_under_limit(numbers_list, 18) elif category.lower() == "adult": new_numbers = return_numbers_over_limit(numbers_list, 17) return new_numbers def classify_age(age, child_designation, adult_designation): designation = "unknown" if (age in child_designation): designation = "child" elif (age in adult_designation): designation = "adult" return designation def rewrite_ages_column(ages): for age in ages: if is_number(age): ages[ages == age] = str(float(age)) return ages def adults_are_present(age_column, adult_designations): return sum(age_column.isin(adult_designations)) > 0 def check_if_group_belongs_to_certain_gender_category(gender_column, gender_category): return sum(gender_column == gender_category.upper()) == len(gender_column) def return_gender_classification(gender_column, age_column, adult_designations, modified_adult_designations): designation = "all_children" if adults_are_present(age_column, adult_designations): temp_gender_column = gender_column[age_column.isin(adult_designations)] if (check_if_group_belongs_to_certain_gender_category(temp_gender_column, "male")): designation = "male" elif (check_if_group_belongs_to_certain_gender_category(temp_gender_column, "female")): designation = "female" elif (check_if_group_belongs_to_certain_gender_category(temp_gender_column, "unknown")): designation = "unknown" else: designation = "mixed" elif (adults_are_present(age_column, modified_adult_designations)): designation = "mixed" return designation def return_sub_dataframe_with_non_zero_entries(old_dataframe, index): new_columns = [] new_data = [] for col in old_dataframe.columns: if old_dataframe[col][index] > 0: new_columns.append(col) new_data.append(age_breakdown_dataframe[col][index]) new_dataframe = pd.DataFrame(columns=new_columns) new_dataframe.loc[index] = new_data return new_dataframe ## Main Method data = pd.read_csv(csv_file) runaway_ids = return_column_from_dataframe(data, runaway_ad_header).unique() all_ages = return_column_from_dataframe(data, "approximate_age").unique().tolist() categories = ["male", "female", "unknown", "mixed", "all_children"] ratio_age_categories = ["gender_classification", "child", "adult", "unknown", "ratio", "women_count", "men_count"] age_classifications = ["child", "adult", "elderly", "unknown"] characterization = ["runaway_id", "classification", "women", "men", "unknown_gender", "children", "adults", "unknown_age"] temp_ages = [] start = [] under_limit_ages = [] children_group = [] adult_group = [] modified_adult_group = [] numerical_ages = return_sorted_numbers_in_list(all_ages) start = append_list_of_entries_to_list(start, [str(age_classifications[0])]) start = append_list_of_entries_to_list(start, numerical_ages) start = append_list_of_entries_to_list(start, age_classifications[1:]) children_group = append_list_of_entries_to_list(children_group, [age_classifications[0]]) children_group = append_list_of_entries_to_list(children_group, identify_ages_in_category(numerical_ages, "child")) adult_group = append_list_of_entries_to_list(adult_group, identify_ages_in_category(numerical_ages, "adult")) adult_group = append_list_of_entries_to_list(adult_group, age_classifications[1:3]) modified_adult_group = append_list_of_entries_to_list(modified_adult_group, identify_ages_in_category(numerical_ages, "adult")) modified_adult_group = append_list_of_entries_to_list(modified_adult_group, age_classifications[1:]) under_limit_ages = return_numbers_under_limit(numerical_ages, collect_data_under_this_age_in_yrs) all_ages = start group_breakdown_dataframe = initialize_empty_data_frame_with_categories(categories) age_breakdown_dataframe = initialize_empty_data_frame_with_categories_and_indices(all_ages, ["female", "male", "mixed"]) runaway_event_characterization = initialize_empty_data_frame_with_categories(characterization) runaway_event_characterization = runaway_event_characterization.drop(0) child_to_adult_ratio_dataframe = initialize_empty_data_frame_with_categories(ratio_age_categories) child_to_adult_ratio_dataframe = child_to_adult_ratio_dataframe.drop(0) for id in runaway_ids: chosen_people = data.loc[data[runaway_ad_header] == id] genders = return_column_from_dataframe(chosen_people, gender_header) ages = return_column_from_dataframe(chosen_people, age_header) ages = rewrite_ages_column(ages) gender_classification = return_gender_classification(genders, ages, adult_group, modified_adult_group) group_breakdown_dataframe.loc[0][gender_classification] = group_breakdown_dataframe.loc[0][gender_classification] + 1 men_count = sum(genders == "MALE") women_count = sum(genders == "FEMALE") adult_count = len(ages[ages.isin(adult_group)]) child_count = len(ages[ages.isin(children_group)]) unknown_count = len(ages) - adult_count - child_count if adult_count > 0 and child_count > 0: ratio = child_count/adult_count else: ratio = "N/A" child_to_adult_ratio_row = [gender_classification, child_count, adult_count, unknown_count, ratio, women_count, men_count] characterization_row = [id, gender_classification, women_count, men_count, len(ages) - women_count - men_count, child_count, adult_count, unknown_count] runaway_event_characterization.loc[len(runaway_event_characterization)] = characterization_row if (gender_classification != "all_children"): child_to_adult_ratio_dataframe.loc[len(child_to_adult_ratio_dataframe)] = child_to_adult_ratio_row for age in ages: age_breakdown_dataframe.loc[gender_classification][age] = age_breakdown_dataframe.loc[gender_classification][age] + 1 ## Re-Define Young Age Groups for ind in age_breakdown_dataframe.index: under_tally = 0 new_columns = [] new_data = [] for col in age_breakdown_dataframe: if col in under_limit_ages: if under_limit_ages.index(col) < len(under_limit_ages) - 1: under_tally = under_tally + age_breakdown_dataframe[col][ind] elif col in under_limit_ages: if under_limit_ages.index(col) == len(under_limit_ages) - 1: under_tally = under_tally + age_breakdown_dataframe[col][ind] new_name = "< " + str(collect_data_under_this_age_in_yrs) + " year" if (collect_data_under_this_age_in_yrs != 1): new_name = new_name + "s" new_columns.append(new_name) new_data.append(under_tally) still_adding = False else: new_columns.append(col) new_data.append(age_breakdown_dataframe[col][ind]) if age_breakdown_dataframe.index.to_list().index(ind) == 0: temp_dataframe = pd.DataFrame(columns=new_columns, index=age_breakdown_dataframe.index) temp_dataframe.loc[ind] = new_data new_columns = [] for col in temp_dataframe.columns: if (is_decimal(col) or col.isnumeric()) and float(col) < use_months_for_age_under_this_in_yrs: month_number = (round(float(col)*12)) new_name = str(month_number) + " month" if month_number != 1: new_name = new_name + "s" new_columns.append(new_name) else: new_columns.append(col) second_temp_data_frame = pd.DataFrame(columns=new_columns) for ind in age_breakdown_dataframe.index: new_row = [] for col in temp_dataframe.columns: new_row.append(temp_dataframe[col][ind]) second_temp_data_frame.loc[ind] = new_row age_breakdown_dataframe = second_temp_data_frame ## Separate Out Age Count by Gender women_age_data_no_zeros = return_sub_dataframe_with_non_zero_entries(age_breakdown_dataframe, "female") men_age_data_no_zeros = return_sub_dataframe_with_non_zero_entries(age_breakdown_dataframe, "male") mixed_gender_age_data_no_zeros = return_sub_dataframe_with_non_zero_entries(age_breakdown_dataframe, "mixed") group_breakdown_dataframe.to_csv('Group_Count_By_Gender_Composition5.csv', index=False) women_age_data_no_zeros.to_csv('Age_Count_For_All_Women_Groups5.csv', index=False) men_age_data_no_zeros.to_csv('Age_Count_For_All_Men_Groups5.csv', index=False) mixed_gender_age_data_no_zeros.to_csv('Age_Count_For_Mixed_Gender_Groups5.csv', index=False) 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\n \n \n

`;\n\n const [prevBtn, nextBtn] = div.querySelectorAll(\"button\");\n\n prevBtn.onclick = () => {\n const newIndex = Math.max(0, currentIndex - 1);\n mutable currentIndex = newIndex;\n };\n\n nextBtn.onclick = () => {\n const newIndex = Math.min(imageData.length - 1, currentIndex + 1);\n mutable currentIndex = newIndex;\n };\n\n return div;\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":418,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":69,"value":"viewof escapeYearSlider = Inputs.range(\n [d3.min(gewc_nodes1, d => d.escape_year), d3.max(gewc_nodes1, d => d.escape_year)],\n {\n label: \"Show Runaways up to Year\",\n step: 1,\n value: d3.max(gewc_nodes1, d => d.escape_year)\n }\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"viewof groupSizeSlider = Inputs.range([2, 20], {\n label: \"Minimum Group Size\",\n step: 1,\n value: 1\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":443,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":790,"value":"## Freedom Seeker Counts and Demographics\n\nRunning from bondage with children in tow was even more dangerous than doing it alone or with a group of other adults. Taking on that mission required strategy of group size and composition.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":281,"value":"Plot.plot({\n title: \"Number of Groups by Group Size\",\n caption: \"Figure 1. Visualizing the trend of group size across all 122 shows that children were most likely to be liberated by smaller groups of adults, potentially family units.\",\n x: {\n label: \"Group Size\"\n },\n y: {\n label: \"Number of Groups\"\n },\n marks: [\n Plot.auto(group_size, {x: \"group_size\", y: \"number_of_groups\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":792,"value":"The youngest children necessitated the most preplanning and care before and during the journey. Toddlers and slightly older children who were of talking age ran the risk of sharing secret plans or making noise at inopportune times. Physically, they would be unable to run or walk long distances and need to be carried, but may be heavy enough that they became a burden on the back of the adult they were with. Infants, though smaller and unable to divulge any secrets, were likely dependent on their mothers for breastmilk and were even more unpredictable in terms of noise. These were risks that many parents and kinfolk were willing to take. Some developed strategies to help mitigate these risks. For example, Harriet Tubman is known to have carried opium on her to keep babies asleep during the most precarious parts of the journey.[7] The Freedom on the Move data echos this idea that safety was not always in numbers. The more people, the greater the hazard.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":920,"value":"## The Gender Breakdown\nFinding that most freedom seeking children were saved by smaller groups of adults allows some imaginative thought into what the process of freedom seeking was like for them. However, the question of *who* saved enslaved children remains largely unanswered without looking at gender. This is also where the data from Freedom on the Move could most heftily contribute to the historical record on enslaved men and kinship. Knowing from the broader interactive network that the adults of these 122 groups were not all female, the question then becomes whether the historiographical notion of shared nurturing and kinship networks being predominantly feminine phenomena is accurate.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":918,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":471,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":961,"value":"Plot.plot({\n width: 750,\n title: \"Ages of the Children Saved By Mixed Gender Adult Groups\",\n caption: \"Figure 3. Bar height shows the number of children saved by this group type per age. All ages under 2 have been collapsed together.\",\n marginBottom: 50,\n x: {\n domain: [\n \"child\", \"<2\", \"3\", \"10\", \"12\", \"14\",\n \"15\", \"16\", \"17\"\n ]\n },\n marks: [\n Plot.barY(mixed_ages_children_viz, {\n x: \"age\",\n y: \"count\",\n tip: true\n })\n ]\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"\nThe age distribution of children saved by mixed gender adult groups is the most varied of the three group types. Children whose approximate age is \"child\" are highly represented, but most common age group was children under the age of two. In no other group type are the babies as common. Although some of the adults comprising these groups are marked as \"unknown\" gender, this data suggests that the youngest freedom seekers, infants and toddlers, may have been more likely to be escaped by their parents or at the very least a many gendered family unit, biological or otherwise.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1132,"value":"Plot.plot({\n width: 750,\n title: \"Ages of the Children Saved By Female Only Adult Groups\",\n caption: \"Figure 4. Bar height shows the number of children saved by this group type per age. All ages under 2 years have been collapsed together.\",\n marginBottom: 50,\n x: {\n domain: [\n \"child\", \"<2\", \"4\", \"12\", \"14\", \"15\"\n ]\n },\n marks: [\n Plot.barY(womengroups_ages_children_viz, {\n x: \"age\",\n y: \"count\",\n tip: true\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1134,"value":"This data confirms the historiography of enslaved mothers, that they were often left to care for children in the absence of men, and that they were often motivated to escape because oof their children. Before further investigation, it is important to note that this data does not confirm nor deny the historiographical notion of othermothering or manymothering. However, we can imagine that the relationships between these women and children were diverse.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1063,"value":"Plot.plot({\n width: 750,\n title: \"Ages of the Children Saved By Male Only Adult Groups\",\n caption: \"Bar height shows the number of children saved by this group type per age.\",\n marginBottom: 50,\n x: {\n domain: [\n \"child\", \"<2\", \"10\", \"11\", \"13\", \"15\", \"16\", \"17\"\n ]\n },\n marks: [\n Plot.barY(mengroups_ages_children_viz, {\n x: \"age\",\n y: \"count\",\n tip: true\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":482,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":840,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":838,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":863,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":867,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":865,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":836,"value":"# Appendix\n\nBelow you will find all of the imports, code, and tables necessary to create and run the visualizations used in this paper.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1182,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1225,"value":"mutable currentIndex = 0\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1245,"value":"import {button} from \"@jashkenas/inputs\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"import { ForceGraph } from \"@d3/force-directed-graph-component\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"nodes = FileAttachment(\"gewc_nodes copy.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"edges = FileAttachment (\"gewc_edges@1.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"gewc_nodes1 = await FileAttachment(\"gewc_nodes copy.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"gewc_nodes1","database":"$undefined"},{"id":27,"value":"gewc_edges1 = await FileAttachment(\"gewc_edges@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"gewc_edges1","database":"$undefined"},{"id":276,"value":"\nSELECT group_size, COUNT(*) AS number_of_groups\nFROM (\n SELECT runaway_event_id, COUNT(*) AS group_size\n FROM gewc_nodes1\n GROUP BY runaway_event_id\n)\nGROUP BY group_size\nORDER BY group_size;\n","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"group_size","database":"var:gewc_nodes1"},{"id":916,"value":"group_type_count = await FileAttachment(\"FO_group_type_count@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"group_type_count","database":"$undefined"},{"id":928,"value":"group_type_count_viz = group_type_count.map(d => ({\n group_type: d.group_type,\n count: +d.count\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":948,"value":"mixed_ages_children = await FileAttachment(\"FO_mixed_ages_children.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"mixed_ages_children","database":"$undefined"},{"id":950,"value":"mixed_ages_children_viz = mixed_ages_children.map(d => ({\n age: d.age,\n count: +d.count\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1032,"value":"womengroups_ages_children = await FileAttachment(\"FO_women_groups_ages_children.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"womengroups_ages_children","database":"$undefined"},{"id":1034,"value":"womengroups_ages_children_viz = womengroups_ages_children.map(d => ({\n age: d.age,\n count: +d.count\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1055,"value":"mengroups_ages_children = await FileAttachment(\"FO_men_ages_children.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"mengroups_ages_children","database":"$undefined"},{"id":1056,"value":"mengroups_ages_children_viz = mengroups_ages_children.map(d => ({\n age: d.age,\n count: +d.count\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"$3b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W3c","stdlib":"1","titling":"auto"}} 30:"$2f:body"