Bibliographic record
Abstract
This article is a conceptual piece which explores what it might be like to incorporate marketing theory and notions of cool into the realm of mathematics education in an attempt to elicit mathematical enthusiasm from a specific subset of the female population who often self-select out of mathematics despite their high mathematical aptitude. Focusing on girls from Toronto, Canada, who generally see themselves as part of the mainstream culture, this article speculates as to how these girls understand their relationship to mathematics. The central purpose of this research is to understand whether these girls choose not to pursue mathematics beyond the compulsory level because they are selecting courses to construct their identity on the basis of cool, using the same evaluation process they would when selecting products. Drawing extensively on literature and participant data, this article presents a novel perspective with which to view female disinterest in mathematics. Grounding the empirical data atop the theoretical brings to life the interconnection of perspectives of scholars like Walkerdine, Mendick, Demetriou and Gladwell, illustrating how femininity, consumerism and mathematics are interwoven into the very fabric of our socially constructed reality. This article argues that treating mathematics as a consumer good and marketing it accordingly might give rise to increased mathematical participation and enthusiasm by this particular segment of girls, who rely on identity marketing for many of their consumption decisions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".