Women in the Canadian academic tundra : challenging the chill
Bibliographic record
Abstract
Recently we have seen a heightened awareness of the unequal treatment of women in the academic community in general and, in particular, of how part-time, sessional, and contract positions are being used to exploit academics. Women in the Canadian Academic Tundra is a timely call for action. It is a brave testimony to the persistence and resilience of women who, against many odds, continue to contribute to the academy with energy and determination. Their touching stories will appeal to all working women as well as to scholars of social sciences and women studies, equity groups, human rights advocates, and agents of governments. Unlike many impersonal statistical reports on the subject of inequality, the narratives in Women in the Canadian Academic Tundra describe the personal experiences, both rewarding and frustrating, of women in the often inhospitable academic setting. Full-timers, part-timers, prominent researchers, and high-ranking administrators intersect with immigrant women, Aboriginal women, women of different cultural and ethnic groups, and women who are physically challenged and health impaired. These women come to life through these narratives and observations, offering several centuries of experience in the academy.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.097 | 0.034 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".