representing a diverse canada in political science: power, ideas and the emergent challenge of reconciliation
Bibliographic record
Abstract
This article examines Canadian political science and responses to diversity both in terms of who is included in the profession and their reported experiences. Utilizing extant national surveys, including from the Canadian Political Science Association, the findings show that in comparison to the 1970s, the profession today is clearly more “diverse” both in terms of its demographics, as well as what is researched and taught. This in turn relates to changing perspectives affecting policy, practice and research both in Canada and internationally. However, as will also be shown, there are evident and persistent structural inequities in the Canadian academy and the discipline of political science that have deep roots in Canada. Of particular importance in explaining these patterns is Canada’s foundation and legacy as a settler-colony, a feature thrown into sharp relief in light of current efforts at “reconciliation” between Aboriginal and non-Aboriginal Canadians. It is therefore argued that the state of diversity in Canadian political science needs to be understood in relation to both evolving ideas as well as the historical formation of the Canadian state and social power.
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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.081 | 0.085 |
| Scholarly communication | 0.039 | 0.010 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".