Bibliographic record
Abstract
Geography is perceived to be a relevant contributing discipline within a growing Canadian studies community in the United States, and the Association of American Geographers (AAG) retains a viable Canadian Studies Specialty Group. Since the early 1990s, however, the number of American geographers affiliated with Canadian studies organizations has not significantly increased: the community of scholars remains small and geography holds a peripheral position in terms of its actual contribution to US‐based Canadian studies programs. This article documents and interprets these trends using membership data collected by prominent professional organizations in Canadian studies and geography. It also explores the question of why American geography's Canadian regional specialists are not engaging formal Canadian studies initiatives in greater numbers. The observations that emerge suggest that a diversification of research themes in Canadian studies, particularly in the realms of environmental and physical science, would both increase participation by American geographers and enhance the field's ability to address pertinent aspects of the Canadian experience. Although observations presented pertain directly to the state of Canadian studies in American geography, they may also shed light on the lack of involvement by geographers in a variety of area studies fields in the United States and elsewhere .
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.068 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".