Assessing Three Elements of “Canadian” International Relations
Bibliographic record
Abstract
Abstract This research note addresses the ongoing debate over the existence of a “Canadian” International Relations (IR) by interrogating the university setting, the professoriate and important institutions of IR in the Canadian context. We not only contribute an update to the data but also enrol a larger number of Canadian universities and a wider sample of journals and conferences. Our analysis is structured around three existing groupings of institutions: the three most “Americanized” departments (the BMT)—University of British Columbia, McGill University and University of Toronto; the four most “critical” departments (the Four Nodes)—McMaster University, University of Ottawa, University of Victoria and York University; and the four largest French-language institutions (the FLIs)—Université de Montréal, Université du Québec à Montréal, Université Laval and Université de Sherbrooke. The characteristic openness often taken to define IR in Canada is more often found at the Four Nodes, the FLIs or unclassified schools than at the BMT schools, which are not only more Americanized in training but also isolated from other Canadian institutions.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".