“He's funny, he's intelligent, he opens doors, a perfect gentleman” : a mixed methods approach to analyzing rape myths within newspaper accounts of the Jian Ghomeshi trial
Bibliographic record
Abstract
A great deal of literature provides an American perspective to the prevalence of rape myths in newspaper accounts of sexual assault cases. The present research attempts to bridge the Canadian gap by first providing a Canadian context to present literature on the prevalence of rape myths within newspaper accounts of the Jian Ghomeshi trial, and secondly, by analyzing the prevalence of positive statements relating to Jian Ghomeshi’s status (i.e. citizenship, good standing, class, privilege, celebrity status). A mixed methods approach combining a content analysis and critical discourse analysis was used to examine articles sampled from four newspapers– The Toronto Star, The Globe and Mail, The Vancouver Sun, and The Vancouver Province. A total of 200 articles were coded for rape myths and positive statements relating to Jian Ghomeshi. Of the articles, 14.5% (n=29) contained at least one rape myth, while 35% (n=70) contained at least one positive statements relating to Jian Ghomeshi. Upon establishing the prevalence of rape myths and positive statements relating to Ghomeshi within the articles, a critical discourse analysis was used in order to better understand how language reproduces and maintains the perpetuation of rape myths and endorses Ghomeshi’s status.
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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 | 0.041 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".