Harassment on Assignment: Gendered Emotional Labour in Canadian Newsrooms
Bibliographic record
Abstract
The harassment of women and gender non-binary journalists is pervasive.In recent years, many scholars and practitioners have collected evidence to suggest it is getting worse.To assess the effects of such harassment in Canada, I interviewed 16 current or former female journalists and one gender non-binary journalist, and conducted a survey of more than 130 journalists to understand the scope of this harassment and its impact, including whether it motivated participants to consider leaving their job.The results of this survey revealed that female and gender non-binary journalists in Canada experience ongoing verbal and physical abuse rooted in misogyny, sexism, racism, Islamophobia and homophobia.Participants detailed cyber-violence, assault, sexual harassment, threats of violence and death threats connected to their work.Racialized journalists, members of 2SLGBTQ+ communities and those covering politics reported some of the most aggressive harassment or violence directed towards them.As a result of these findings, this thesis adds concrete recommendations to growing calls for news organizations and all levels of government to protect women and gender non-binary journalists from the emotional labour and abuse connected to the job.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".