Access, Inclusion, Climate, Empowerment (AICE): A Framework for Gender Equity in Market-Driven Education
Bibliographic record
Abstract
We present a framework for conceptualizing gender equity, designed around four equity components: Access, Inclusion, Climate, and Empowerment (AICE). Our examination of these components in the current market schooling climate, with particular reference to the situation in Ontario, identifies some significant equity costs of market-driven education, including invisibility of systemic discrimination, co-option of gender equity initiatives to serve market objectives, failure to consider diversity and relations of power in educational practices, increased risks of sexual harassment, and increased barriers to social change. AICE equips educators with an analytical tool to conceptualize gender equity in a market-driven schooling climate. Les auteures proposent de conceptualiser le traitement équitable des sexes à l’aide d’un schéma formé de quatre éléments : accès, inclusion, climat et habilitation. L’analyse de ces éléments dans le contexte scolaire actuel, en particulier en Ontario, dévoile d’importants coûts inhérents à l’enseignement axé sur le marché, dont l’invisibililité de la discrimination systémique, l’assimilation aux objectifs du marché des initiatives en matière d’équité entre les sexes, l’occultation de la diversité et des relations de pouvoir dans les pratiques pédagogiques, les risques accrus de harcèlement sexuel et la multiplication des obstacles au changement social. Le schéma donne aux enseignants un outil analytique leur permettant de conceptualiser le traitement équitable des sexes dans un contexte éducatif axé sur le marché.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".