Bibliographic record
Abstract
This multi-disciplinary anthology is about hermeneutical issues pertaining to gender ideology in university scholarship. The authors provide, from their own discipline, an extensive examination of the issues raised in the Social Sciences and Humanities Research Council of Canada pamphlet, "On the Treatment of the Sexes in Research," by Margrit Eichler and Jeanne Lapointe (1985). Gender bias is described and evaluated in the light of possible alternative perspectives which would alter the content and shape of research, including women as subjects of research and as researchers. The authors underscore the importance of acknowledging underlying gender imagery in the selection, interpretation, and communication of research data. They explore the notion of research as a social construction which is strongly aligned with the socially constructed notion of male and dissociated from the socially constructed notion of female. The focus is on refraining research ideology to include both female- and male-constructed imagery. Contributors include Marlene Mackie (sociology), Carolyn Larsen (psychology), Estelle Dansereau (literary criticism), Gisele Thibault (education), Alice Mansell (art), Eliane Leslau Silverman (history), Yvonne Lefebvre (biochemistry), Petra von Morstein (philosophy), and Naomi Black (political science).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".