Gender, the Media and Parity: The Case of the 2018 Québec Election
Bibliographic record
Abstract
Abstract This article investigates the representativeness of news coverage when there are nearly as many female candidates as there are male candidates by considering the 2018 Québec Election, in which 47 per cent of candidates were women. We are interested not only in the magnitude of the coverage (that is, the volume of press coverage received by each candidate) but also in its tone (if the press coverage is negative or positive) and whether these parameters fluctuate based on the gender of the candidates. We know that the quality of the news coverage, and more specifically its tone, can affect voting intentions. We also know that journalists routinely portray politics as a masculine activity, but we know very little about the coverage that local female candidates receive in the context of parity in North America. We find that in spite of exceptional circumstances, female candidates received significantly less coverage than men.
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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 | 0.002 |
| 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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".