Linguistic Cleavages in Canadian Political Science: Evidence from the Discipline's Annual Conference
Bibliographic record
Abstract
Abstract Academics across Canada, an officially bilingual and multicultural country, devote a lot of attention to diversity and representation. This is particularly true for political scientists. In this research note, we focus on the linguistic composition of panels and overall linguistic fragmentation of the most important in-person event for Canadian political science: the annual meeting of the Canadian Political Science Association (CPSA). To do so, we generated a dataset based on the official program of the 2023 annual conference. Our main results are twofold. First, we find an important under-representation of French-speaking events and academic communications (i.e., panels and papers). Second, we computed Herfindahl-Hirschman indexes demonstrating that francophone-dominated panels and co-authored papers with francophone first authors are significantly more linguistically diverse than anglophone panels and papers. Our results highlight important blind spots in Canadian political science and help make sense of the lack of representation of French-language work in Canadian academia.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.033 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".