Nature's Past Episode 046: Historical GIS Research in Canada
Bibliographic record
Abstract
In recent years, environmental historians and other historians have been working with maps in new ways. Specifically, they have been using HGIS software, that is, historical geographic information systems. You may have heard a bit about this already. \n \nHGIS has allowed historians to take historical data and visualize and analyze it spatially. This allows one to present evidence in new ways, but perhaps more significantly, it provides researchers with novel approaches to the analysis of historical data. We can see things in the data with HGIS that we couldn’t see before. \n \nHGIS research has taken off in the field of environmental history. More researchers have been using HGIS as part of what some have called a “spatial turn” in scholarship. Census data, municipal assessment rolls, and aerial photographs, just to take a few examples, can be analyzed and presented in new ways spatially with HGIS software. \n \nGetting started with HGIS can be intimidating and it often requires collaboration among historians, geographers, librarians, and other scholars. To help researchers in the field of environmental history get acquainted with the uses of this technology, the University of Calgary Press and the Network in Canadian History and Environment have published a new book called, Historical GIS Research in Canada. You can read our review of the the book here. \n \nOn this episode of the podcast, we speak with the editors of this new book, Jennifer Bonnell and Marcel Fortin as well as a couple of the contributors. \n \nPlease be sure to take a moment to review this podcast on our iTunes page.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.032 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".