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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".