Torrential Twitter? Measuring the Severity of Harassment When Canadian Female Politicians Tweet About Climate Change
Bibliographic record
Abstract
The online harassment of female politicians who focus on climate change and environmental policy has become a major problem in Canada and other democratic nations. Despite growing awareness of the problem, there is little agreement among scholars on how to measure these nuanced forms of harassment. This study develops an original seven-point scale to measure the severity of harassment three Canadian female politicians receive when Tweeting about climate change and a six-point schema to categorize the types of accounts behind the replies. My results reveal that 86% of replies contained some form of harassment, most often name-calling or questioning the authority of the female politicians, and come from users with spam or anonymous accounts. Further results from my Bayesian hierarchical model suggest that despite differences in status and political affiliation across the three politicians, they are almost equally impacted by harassment when Tweeting about climate change. These findings contribute to understanding the intersection between climate change denialism and the gendered nature of online harassment. This article contains language and themes that some readers may find offensive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".