WOMEN MUNICIPAL POLITICIANS IN ELECTION NEWS
Bibliographic record
Abstract
The news media’s fascination with which party is ahead in the polls — otherwise known as the horse race — has raised ques-tions about how well informed voters are about their choices on the ballot box. A preoccupation with campaign strategies, gaffes, and photo-ops leaves journalists with less time to report on issues and platforms. Some scholars argue women are particular-ly handicapped by horse-race coverage because it can lead to negative evaluations of their electoral viability and because the masculine language used in this type of coverage could depict them as inappropriately aggressive and therefore transgressing traditional gender norms. But this study on newspaper coverage of municipal elections in one Canadian province reveals that journalists treat regular council contests more as a marathon than a horse race. The nature of municipal election coverage suggests journalists treat candidates as a mass group of runners, doing little to distinguish them from each other and rarely spe-culating on their electoral chances. The real problem for women and men council candidates is not media bias but media invisibi-lity—getting the coverage they need to build a public profile so voters will support them. Key words Women politicians, horse-race journalism, municipal politics, Cana-da. 1.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".