Bibliographic record
Abstract
In many developed countries, including Canada, it is often perceived that gender issues in mathematics have been ‘solved’ and are no longer a relevant and timely issue. In this article, I challenge this perception by providing an overview of gender issues in mathematics in three domains – achievement, attitude, and participation – ranging from the elementary school level to the university level. My analysis of several sources of data from Ontario is compared to a meta-analysis of research involving data from culturally similar countries to Canada (e.g., Australia, United Kingdom). The data primarily arise from large-scale mathematics assessments (e.g., PISA, EQAO) and national statistics databases (e.g., Statistics Canada, National Center for Education Statistics). Counter to the aforementioned perception, this analysis indicates that gender issues still exist in mathematics in developed countries, including Canada. The gender gap is particularly wide in terms of students’ attitudes and participation: Males have substantially more positive attitudes toward mathematics and higher levels of participation in non-mandatory levels of mathematics than do females. The article concludes with a discussion of the implications of the findings and suggestions of possible steps that may be taken to help ameliorate the current situation.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".