Gender Bias in Canadian Politics: A Content Analysis of a Canadian Prime Minister’s Speeches in 2015
Bibliographic record
Abstract
This study examines the way former Prime Minister Stephen Harper addresses men and women in his speeches, and how this political rhetoric can manipulate media messaging. We performed a content analysis of individuals mentioned in a sample of Stephen Harper’s 2015 speeches using seven coding categories: Name, Age, Sex, Occupation, Title Used, Speech Mentions, and Total Mentions. The research design follows a sequential explanatory style, supplementing quantitative findings with qualitative analysis. We hypothesize that Harper’s speeches contribute to the unfair representation of women in politics by continuing the patriarchal cultural practice of overlooking, belittling, or ignoring female accomplishments; this translates to unfair treatment (i.e. stereotyping, under- or mis-representation) of women in mainstream media. This research is a continuation of Dr. Peter Ryan’s research on Harper’s speeches from 2004–2014, and contributes to this previous research by shedding light upon the relationship between gender and politics in Canada. The results of the current study show that there is a disparity in the way that Stephen Harper referenced men and women: not only are women mentioned less frequently, but they are also less likely to be the focus of the speech and to be given a formal title. The literature supports the notion that this disparity inevitably results in a distorted representation of women in 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".