Green with rage: Women climate change leaders face online attacks
Bibliographic record
Abstract
<p>Women leaders who support climate action are being attacked online with increasing regularity. These attacks should be viewed as a problem not only for the planet, but also to the goals of achieving gender equality and more inclusive, democratic politics.</p> <p>Catherine McKenna, Canada’s environment and climate change minister, recently announced that she’s had to hire security to protect herself and her family while in public. With an election now on, it’s likely she’ll face further abuse in the weeks to come.</p> <p>McKenna hired security after she was out with her children and a driver rolled down his window and shouted: “F-k you, Climate Barbie.” This sexist taunt was popularized by Conservative MP Gerry Ritz, who once used the slur in reference to McKenna on Twitter.</p> <p>It resulted in a tsunami of #Climatebarbie hashtags and variations of the slur ever since. Ritz has since apologized and deleted the original tweet.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".