Bibliographic record
Abstract
I examine the gendered nature of university adult education and how women continue to be disadvantaged in the "games" which characterize many graduate and professional programs. How can we change the "rules," or substitute "girls'" rules, for those in play? I suggest that, as women working for change, we first need to acknowledge the discursive practices and structures of university adult education as a "boys' game." I intersperse stories of women learning at university (drawn from my empirical research with women, and my everyday experience), with theoretical insights from post-modern and post-structural scholarship. I suggest we maintain the strategic essentialism of the feminist project, and work within the gendered spaces available to us. We can then make combined use of both gender and liberatory models of feminist pedagogy to improve women's learning experiences in their graduate education. Résumé Dans cette étude, j'examine la formation permanente en milieu universitaire sous l'angle des rapports hommes-femmes et la manière dont les femmes continuent à se trouver désavantagées par ces «jeux» qui caractérisent nombre de programmes d'études supérieures et de formation professionnelle. Comment pouvons-nous changer les règies du jeu ou leur en substituer qui soient conformes à notre manière de jouer, nous, les femmes? Je propose que, en tant quefemmes engagées pour le changement, nous devons tout d'abord reconnaître le caractère masculin des pratiques discursives et des structures de la formation permanente en milieu universitaire. J'emaille cet article d'histoires de femmes qui étudient à l'université (elles sont tirées de ma propre recherche empirique auprès de femmes ainsi que de mon expérience personnelle), en y ajoutant des perspectives théoriques fondées sur la recherche dans les domaines du post-modernisme et du post-structuralisme. Je propose que nous conservions l'essentialisme stratégique du projet féministe tout en travaillant au sein des espaces où s'exercent les rapports hommes-femmes et qui nous sont accessibles. Ensuite, nous pouvons également combiner les modèles de rapports hommes-femmes et les modèles de libération afin d'améliorer les expériences d'apprentissage des femmes dans leur programme d'études supérieures.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".