Using GIS and Mapping Tools to Access and Visualize Archival Records: Case Studies and Survey Results of North American Archivists and Historians
Bibliographic record
Abstract
Online map interfaces and GIS software are means of accessing and visualizing archival holdings associated strongly with places. This article investigates the possibility of an interest among at least some archivists and historians in finding records based on place names and maps. A review of recent tools and case studies on map-based methods of seeking and visualizing information in archives and special collections provides a current overview. A 2015 survey gathered additional information from archivists as to whether they place a high priority on, and are comfortable with, map-based methods, as well as to what extent their patron groups might benefit from such methods. A subsequent 2018 survey of historians provided evidence that this major patron group of archives would benefit from map-based methods of discovery, although the survey indicated that they are focused on GIS software, not simple visualization tools, in their own work. The literature and survey data validate the premise that many archives patrons are interested in exploring this area, but that the difference between archivists’ and historians’ technical knowledge and interests is a significant obstacle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".