Academic Absences, Disciplinary Siloes and Methodological Prejudices within the Political Science Discipline in Canada
Bibliographic record
Abstract
Abstract Canadian political science has changed over the past 50 years; however, these changes have come slowly and lag behind larger societal demographic transformations. While early attention to diversity concentrated on the place of women within the discipline, more recent attention focuses on the presence of Black, Indigenous and other political scientists of colour. Accompanying a diversification of personnel has been a broadening of the substantive focus of our research, as well as an expansion in the epistemological and methodological approaches applied to the study of politics. Yet despite these adaptations, the study of political science in Canada remains siloed and often exclusionary, challenging our ability to train the next generation of scholars to be capable of addressing the issues facing a world that is increasingly complex and diverse.
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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 | 0.050 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.039 | 0.024 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".