Bibliographic record
Abstract
Fundamentally concerned with place, and our ability to understand human relationships with environment over time, Historical Geographic Information Systems (HGIS) as a tool and a subject has direct bearing for the study of contemporary environmental issues and realities. To date, HGIS projects in Canada are few and publications that discuss these projects directly even fewer. This book brings together case studies of HGIS projects in historical geography, social and cultural history, and environmental history from Canada's diverse regions. Projects include religion and ethnicity, migration, indigenous land practices, rebuilding a nineteenth-century neighborhood, and working with Google Earth. With contributions by: Colleen Beard Stephen Bocking Jennifer Bonnell Jim Clifford Joanna Dean François Dufaux Patrick A. Dunae Marcel Fortin Jason Gilliland William M. Glen Megan Harvey Matthew G. Hatvany Sally Hermansen Andrew Hinson Don Lafreniere John S. Lutz Joshua D. MacFadyen Daniel Macfarlane Jennifer Marvin Cameron Metcalf Byron Moldofsky Sherry Olson Jon Pasher Daniel Rueck R. W. Sandwell Henry Yu Barbara Znamirowski
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.029 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".