Snips and Snails and Puppy Dogs' Tails: Genderism and Mathematics Education.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In mathematics education over the last four decades, researchers have attended to gender more than other issues of equity (Lubienski & Bo wen, 2000). However, the troubles of boys have not been a major focus, perhaps because of historic achievement gaps that favor boys. As Leder and Forgasz (2008) point out, although achievement gaps between boys and girls have narrowed or closed in many countries, substantial gaps still exist. They argue that the field is simply not attending to or measuring the right gaps. As a result, public discussion of the problems of boys and academic discussion of mathematics achievement for girls do not seem to have many points of connection. While in other areas of educational research (especially literacy or school discipline) it is commonplace to discuss the troubles of boys, such discussion is sparse in mathematics education research. However, as the boy turn gains ground locally and internationally, mathematics educators will be called upon to respond. In this paper, I use the concept of genderism to consider what that response should be. I will begin, though, by highlighting one specific example of the boy turn in education, one that is close to home for me: the recent comments by Toronto District School Board (TDSB) director Dr. Chris Spence about the need for targeted support for boys in TDSB schools.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it