Gendered discussion of politicians in news : how can we prepare future female politicians for media gender bias?
Bibliographic record
Abstract
This research adds to the pre-existing knowledge of gender bias towards women in media. Through a focused review of political news media in British Columbia, this research found little evidence of gender bias towards women written in both municipal and provincial political news reporting. I conducted quantitative content analysis by reviewing 100 online articles during the calendar year 2014 from B.C. newspapers The Province and The Vancouver Sun, including comments in response to these articles posted online by the public, to find the frequency of gender biased language used to describe female and male politicians in these mediums. I also conducted three interviews of female politicians from British Columbia and analyzed them using qualitative content analysis. Both the online content and interview data were used to create a document tool, Appendix B, of best practices for female politicians to refer to when preparing and relaying their messages to the media. This was done to create better understanding of female gender bias so a more gender equal political news reporting environment can be created. My findings indicated a perceived gender bias in political news media reporting by the three interview subjects, but very little indication of gender bias in the political news reporting of major provincial newspapers in British Columbia. The results suggest gender bias was created more by the community and fellow politicians of British Columbia than print news 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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".