Bibliographic record
Abstract
This study focuses on gender inequality in representations of “expert” women in the media. Existing scholarly literature has demonstrated that, in general, women are underrepresented or portrayed as objects or victims in the media. Very little of this literature has examined depictions of “expert” women. The research is guided by this question: How are “expert” women depicted in contemporary Canadian newspapers? The methodologica l framework of the research is a mixed-methods approach using discourse analysis as methodology and content analysis as the concrete method. The data were collected from all news pages of the National Post and the Globe and Mail. Content analysis data were chosen monthly from the first day of each month of the year 2015 for both newspapers. Data for discourse analysis were selected from all issues in November 2015, since the event of Justin Trudeau’s selection of a gender-equal cabinet occurred in this period of time, putting gender, representation, and expertise at the center of a national conversation. Drawing upon the content analysis, I examined the hypothesis of the existence of gender inequality in both national newspapers. The findings confirm that there is gender disparity in newspapers’ representation. Studying news stories about Justin Trudeau’s selection of a gender-equal cabinet through discourse analysis helped to identify some particular discourses that reinforce and reproduce gender inequality—not only in the news stories but also in society more broadly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".