The Library as Research Partner and Data Creator: The Don Valley Historical Mapping Project
Bibliographic record
Abstract
The emergence of new academic disciplines that embrace geospatial analysis allow GIS librarians to be involved in collaboration and outreach work that promotes the academic library and the subject and technical expertise of GIS librarians. The Don River Valley Historical Mapping Project (DVHMO) is an example of such a scholarly collaborative project. At the University of Toronto, the map and GIS librarian, Marcel Fortin, collaborated with PhD history student Jennifer Bonnell (currently a history postdoctoral fellow at the University of Guelph) in a historical mapping project that would promote and breathe new life into library resources. By drawing on paper-based, historical geographic resources, such as fire insurance plans, topographic maps, county atlases, and planning and conservation reports, they built several geospatial historical data sets of shorelines, industries, and landownership data. The digital data sets are now available free online, and intended to be used by researchers and curious citizens alike. Through projects like these, GIS librarians develop services that draw on their longstanding expertise of maintaining collections and promoting free and open access to information. At the same time, they demonstrate how librarians can become the architects of new information. They thereby assert the skills of academic librarians as research partners and data creators.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".