SMART GIRLS, HARD‐WORKING GIRLS BUT NOT YET SELF‐ASSURED GIRLS: THE LIMITS OF GENDER EQUITY POLITICS
Bibliographic record
Abstract
Higher levels of girls and women’s participation in targeted areas are widely apparent, particularly in affluent and middle ‐ class sites. Here, we report on research with young middle and upper middle ‐ class high school girls successfully enrolled in non ‐ traditional advanced placement (AP) courses in mathematics, science, and computer programming in a suburban school district in Midwestern USA. Focus group inter ‐ views with 45 of the highest achieving students in this affluent suburb revealed salient inequities and lingering impediments in the struggle for women’s equality. Likewise, the limitations of gender equity politics are evident in the co ‐ opting of discourse of privilege and individualism. Key words: girls’ achievement, secondary schools, girls in advanced placement study, AP classes Une presence accrue des filles dans les cours avances de mathematiques, de science et d’informatique au secondaire est manifeste, surtout dans les milieux aises aux E. ‐ U. et dans d’autres pays occidentaux. Cette recherche quantitative, effectuee aupres de jeunes filles de classe moyenne ou superieure inscrites dans des cours de niveau avance de mathematiques, de science et d’informatique au secondaire dans un arrondissement scolaire du Midwest americain, revele des inegalites importantes et des obstacles persistants dans la lutte des femmes pour une pleine egalite, ce qui montre les limites des politiques en faveur de l’equite entre les sexes. Mots cles : equite entre les sexes, filles et mathematiques avancees, cours de science, cours d’informatique, filles et science, cours de mathematiques
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".