What Counts? Examining Academic Values and Women's Life Experiences from a Critical Feminist Perspective
Bibliographic record
Abstract
Women comprise the majority of students in adult education, but the experiences of women continue to be marginalized and devalued in the academic sphere. This article provides an exploration of the current critical analysis of the marketplace influence on adult education, and a comparative feminist analysis with a critical perspective. I argue for the development of a critical feminist approach to adult education that acknowledges the importance of women's life experiences and connections to the homeplace. Various research studies are correlated with my own experience to provide an alternative value set from which to challenge existing marketplace influences within academia, and redefine the way society assesses "what counts" as important work and learning experiences. The critical discourse I advocate supports the work that women do in the homeplace, as students and as academics; the process can lead to a more holistic and inclusive approach to adult education. Résumé Bien que les femmes représentent la majeure partie de l'effectif étudiant dans les programmes de formation pour adultes, leurs expériences continuent à être marginalisées et dévalorisées dans les milieux universitaires. Cet article explore l'analyse critique actuelle portant sur l'influence du marché dans le domaine de la formation pour adultes et offre une analyse féministe comparative fondée sur une perspective critique. J'y prône le développement d'une approche féministe critique en matière de formation pour adultes, qui tienne compte de l'importance des expériences vécues par les femmes et des liens qu'elles entretiennent avec la maison. L'article présente diverses études qui corroborent mon expérience et mettent de l'avant un nouvel ensemble de valeurs. Ces valeurs contribuent à remettre en question l'influence du marché dans les milieux universitaires et à redéfinir la manière par laquelle on évalue 'ce qui compte' vraiment sur les plans du travail et des expériences d'apprentissage. Par ce discours critique, j'entends appuyer le travail que font les femmes à la maison, en tant qu'étudiantes et en tant que chercheurs; ce processus est susceptible de mener à une approche en matière de formation pour adultes qui soil plus globale et respectueuse des différences.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".