The Gender Paradox in School Mathematics
Bibliographic record
Abstract
Current quantitative gender and mathematics research exists as separate ability and postsecondary access studies, each drawing different conclusions. But if gender, achievement and enrollment (measurable proxies) are modeled together it will be possible to investigate possible associations. Using 106,473 standardized Grades 3, 6 and 9 Ontario student test responses from a single cohort, likelihood of enrolling in Grade 9 academic mathematics was modeled against elementary achievement and gender in a binary response model. Males achieved higher, (0.028≤d≤0.118), and occupied both achievement extremes in greater numbers, (1.04≤VR≤1.10), while females were 1.5 times more likely to enroll in academic courses. These paradoxical results are discussed in relation to the utility of achievement and enrollment as research metrics in gender and mathematics research. La recherche quantitative actuelle sur la différence en mathématiques entre les filles et les garçons se déroule sur deux plans distincts, un portant sur les habiletés et l’autre sur l’accès aux études secondaires, et les deux arrivent à des conclusions différentes. En modélisant ensemble le sexe, le rendement et les inscriptions (passerelles mesurables), il est possible d’étudier des associations possibles. Puisant dans 106 473 réponses aux examens normalisés d’une seule cohorte d’élèves en 3e, 6e et 9e années en Ontario, nous avons modelé, dans un modèle de réponse binaire, la probabilité de s’inscrire à des cours de mathématiques théoriques en 9e année en relation avec le sexe et le rendement à l’école élémentaire. Le rendement des garçons était supérieur (0.028≤d≤0.118) et leurs réponses se situaient aux deux pôles de rendement plus souvent (1.04≤VR≤1.10), alors que les filles s’inscrivaient aux cours académiques 1,5 fois plus souvent que les garçons. Nous discutons de ces résultats paradoxaux par rapport à l’utilité du rendement et de l’inscription comme mesures dans la recherche portant sur la différence en mathématiques entre les filles et les garçons.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".