Bibliographic record
Abstract
Since the 1990s, Canadian federal election studies show that women and men do not hold equal amounts of campaign knowledge. The political science literature suggests that changed gender roles, increased feminist socialization, and improved socio-economic resources over time should have eliminated the gender differences over time. Acquiring and maintaining political knowledge, however, is a complex phenomenon. I argue that the gender gap can be explained by taking into account women and men’s individual and aggregate-level political resources, personal motivations, cognitive engagement in electoral campaigns, and their roles as mothers and fathers. First, I test the conventional explanations of the gender gap. Using the 1997 to 2008 Canadian Election Studies, I examine the impact of the individual-level socio-economic status, gender role change, and individual political motivation on women and men’s knowledge of campaign facts. Even with these factors included in the model, the gender gap in knowledge of party leaders remained ten points in favour of men and for party promises eight points in favour of men. Two alternative explanations of the gap are then tested. First, I examine the gender gap during the five-week Canadian federal election campaign. Flooded by political coverage in media, political advertising and political discussion, the impact of gender changes across the campaign. I find that the rate change in providing correct responses is different for women and men during federal elections campaigns, which suggests that women engage later in the campaign compared to men. The gendered rate change in providing correct responses does not change the overall gender gap, however. Second, I test the impact of political resources at the local level on the gender gap in campaign knowledge using the 2006 Canadian Census. The analysis shows that compared to men, women’s knowledge of the party leaders is positively affected by the employment rate at the constituency level. Local education rates, on the other hand, have an impact on neither women nor men’s knowledge of party leaders. Women living in areas with the highest employment rate provided correct responses five percentage points higher than women living in areas with the lowest rate of employment on average.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".