Historical map digitization in libraries: Collaborative approaches for large map series
Bibliographic record
Abstract
Academic libraries are playing a role in the digitization of Canadian government documents, but maps tend to be excluded from these activities due to their unique dimensions and display requirements. Using a topographic map digitization project as a case study, this paper presents a collaborative approach to map scanning, georeferencing, and metadata creation across several Ontario universities. Collectively, the 21 institutions making up the Ontario Council of University Libraries (OCUL) possess and maintain large volumes of Canadian topographic maps. However, few OCUL universities hold complete sets of these map series. While the Canadian government’s most recent topographic maps are now available online, older editions of these maps have not been digitized. This project, currently underway at several participating universities, will enable us to share digital versions of some of our most-requested historical map series with the public at large.
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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 | 0.042 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".