Beyond the Gender Gap: Presidential Address to the Canadian Political Science Association, Saskatoon, 2007
Bibliographic record
Abstract
Abstract. This article identifies a number of potential pitfalls in pursuing research on the gender gap phenomenon, including the risk of categorical thinking, reinforcing gender stereotypes, inviting normative comparisons and creating unrealistic expectations about the emergence of a “women's voting bloc.” It highlights the extent to which studies of gender gaps in vote choice and public opinion have adopted a female-centred perspective and calls for greater attention to the role of men in driving the process of gender re-alignment. It then uses data from a survey of women in Toronto and Montreal to illustrate the importance of moving beyond the gender gap to understand the differences that exist among women, especially along the lines of class and racial background. Résumé. Le présent article décrit un certain nombre de pièges pouvant surgir lors des recherches sur l'écart entre les sexes, notamment le risque de la pensée catégorique, le renforcement des stéréotypes, l'invitation aux comparaisons normatives et la création d'attentes irréalistes quant à l'émergence d'un “vote des femmes en bloc”. L'article souligne à quel point les études portant sur l'écart entre les sexes dans le choix électoral et dans l'opinion publique ont adopté une perspective féminine, et il suggère que plus d'attention soit accordée au rôle de l'homme dans le processus du réalignement des sexes. Les données d'un sondage sur les femmes à Toronto et à Montréal illustrent l'importance de transcender l'écart entre les sexes pour comprendre les différences, fondées notamment sur la classe et la race, qui existent entre les femmes elles-mêmes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.005 |
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".