Geohistory: Crowdsourcing and Democratizing the Landscape of Battle
Notice bibliographique
Résumé
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...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».