Bibliographic record
Abstract
GeohistoryCrowdsourcing and Democratizing the Landscape of Battle T. Lloyd Benson (bio) From the bands of slave refugees who provoked a national crisis by escaping their bondage and the waves of troops at places such as Antietam, Chickamauga, and Petersburg to the ongoing march of postwar visitors to the nation’s battlefields, the Civil War has been a story of crowds engaging the landscape. Many soldiers and civilians came in centrally organized groups, volunteers in name only. Others engaged the landscape of conflict more informally, taking photographs, mapping or marking sites of personal memory, reminiscing, communing, or discovering. A few crossed these landscapes more clandestinely, erasing, appropriating, scavenging, or fleeing. Whether sanctioned or illicit, their spontaneous expressions have provided historians with an unparalleled array of materials. In our own time, new collaborative digital humanities tools and geographic information systems (GIS) have opened rich opportunities for Civil War history. By harnessing the energy of public contributors, historians can bring forgotten documents to new audiences, make older materials available in digital format, and supply researchers with new insights about the evolving physical and mental landscape and environment of war, all while inviting broader participation in the scholarly conversation.1 Digital scholars have created the term “crowdsourcing” to describe these shared collection efforts. Originally applied to the outsourcing of corporate tasks, the term now encompasses a broad variety of open collaborations where participants volunteer their work and information to a common project or resource. As other disciplines have shown with initiatives such as “citizen science,” there are tremendous benefits when scholars collaborate with a supportive and engaged volunteer public to gather and share information. Because many of the most innovative crowdsourcing tools provide us new ways to visualize spatial data this essay will focus on the landscapes of battle as a useful heuristic. Readers, however, should infer many broader applications of these tools and methods.2 [End Page 586] While digital crowdsourcing is new, however, it is worth noting that the concept of lightly organized public contributions was widely practiced on the Civil War landscape from the time of the war itself. Indeed, from the broad-based literacy of Civil War veterans emerged a new literary genre, the regimental history, celebrating the contributions of common soldiers and their leaders. As Jim Weeks has documented for Gettysburg and Timothy Smith and others shown for many other battlefields, the initiatives of private entrepreneurs, reputation-boosting generals, veteran-conscious political leaders, and soldiers determined to mark the ground they fought on were only the most influential of the many actors who crowdsourced that era’s landscape of commemoration.3 Likewise, maps and atlases have been a staple of Civil War history since before the first diagrams of Manassas appeared in leading newspapers. It seems impossible to tell the era’s story apart from geography and place.4 Yet only recently have scholars begun to realize how fully the conflict and its commemoration were bound by nineteenth-century notions of landscape and territory and how these ideas were themselves a source of bitter dispute. Far from ceasing with time, these debates have only become more intricate as the assortment of people asserting claims over the Civil War’s interpretive landscape has broadened and as other interests have demanded control over battleground space.5 Crowdsourcing presents Civil War historians with intriguing new scholarly pathways into these historical landscapes. Such collaborations provide new ways to link, overlay, compare, data-mine, and interpret existing materials. They offer tremendous promise in helping us to understand more deeply and systematically the war’s ongoing place in public memory and the wide range of purposes visiting crowds find to use various Civil War historical sites. Crowdsourcing’s potential for interdisciplinary overlays and its definitional capacity for multiple perspectives will allow us to better place our particular subfield into a more global and interdisciplinary context. Its potential can be seen in a number of exemplary pioneering projects involving cultural memory and community geography. The Murmur Project of Toronto, for example, allows participants and users to share their own oral histories of public spaces and interact with those of others as they move through the urban environment. Canada’s related Memory Project, which enables digital...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".