Comparison of Progressive and Conservative Representations of Men’s Mental Health in Written News Media
Bibliographic record
Abstract
Men’s mental health has long been stigmatized in Western society. The media plays a substantial role in emphasizing the importance of mental health; however, a gender disparity exists as men are often less highlighted than women in regard to this subject. This study investigated whether a difference in men’s mental health portrayal exists between progressive and conservative news media in the United Kingdom. Using Factiva, eight news articles were analysed; these included: The Guardian, The Daily Mirror, The Daily Telegraph, and The Times, yielding a sample size of 32. Five criteria were established to score the articles on a Yes (1) or No (0) scale. An ANOVA and a t-test were used to determine the statistical significance of the results. The analyses showed significantly higher scores for progressive news media than conservative news media, whereby The Guardian had the highest percentage of articles that included criteria 1 through 4. The findings revealed a significant difference between how men’s mental health is portrayed in progressive versus conservative news media. Specifically, there was a better representation of men’s mental health in progressive news outlets. Since a limited number of papers were analysed, further research should be conducted to better understand the portrayal of men’s mental health in the media.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".