Not Seen, Not Heard: Gender Representation on Canadian Television News during the Leadup to the 2011 Federal Election
Bibliographic record
Abstract
<p>The findings suggest that a significant majority of clips came from men. On average, women account for less than one fifth of the interview clips. Women are sometimes used as non-experts, and interviewed when an opinion is solicited from an “average” person on the street. However, when expert opinion is sought, women are interviewed less often than men. In addition, when partisan opinion is solicited, women are rarely consulted. Interestingly, there is no correlation between whether the reporter is male or female. Female reporters are as likely to interview male experts, as male reporters are to use male interview subjects. This dearth of women on the air is important. Those given air time during events such as elections have the opportunity to influence national discourse. In addition, the priorities and observations of those interviewed have the potential to influence political campaigns. Traditional women’s issues, such as childcare, may be seen to carry less weight with the population at large if few women are given a forum where they might express their opinions. This paper presents the research findings and explores the implications for journalism school students and the public at large.</p> <p> </p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".