Gender Representation on Canadian Television News during Provincial Elections: A Longitudinal Study
Bibliographic record
Abstract
<p>This article uses content analysis to investigate coverage on CBC, CTV, and Global television networks in the crucial one-month periods leading up to the last three elections in Ontario, Canada. The research addresses both the number and length of clips (sound bites) from women, as compared to clips from men. It measures clip counts, airtime, and explores whether clips came from candidates, partisans (working for a party), experts, or non-experts. The findings from this content analysis indicate that a majority of clips came from men. Significantly, two of the networks have more “expert” clips from men than women over all three elections, and all three networks have more clips from men than women in the elections of 2007 and 2011. This means that pundits who comment upon and analyze provincial elections are usually male. This article explores the implications of these findings and considers confounding factors such as evidence that women may be more reluctant to offer expert opinion, even when they are as qualified as the men who do so. The analysis is important because those given airtime during events such as elections have the opportunity to influence discourse. In addition, the priorities and observations of those interviewed have the potential to influence political campaigns. Women’s issues, for example, may be seen to carry less weight with the population at large if fewer women than men are given a public forum where they might express their opinions.</p> <p> </p>
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".