Gender biases and hate speech: promoters and targets in the Argentinean political context
Bibliographic record
Abstract
Hate speech found in social media a place to flourish. In the Argentinean context, new right wing parties have disrupted the political arena, winning the primary elections of 2023. Many of these new right-wing figures grew in popularity due to their use of social media, on a background of increasing political violence. In this article, we use quantitative and qualitative tools to investigate the prevalence of hate speech targeting women politicians and analyze the role of different political affiliations in promoting such discourse. Furthermore, we propose a model that predicts users' political alignments based on their profile descriptions, allowing us to explore the distribution of hate speech among different political orientations. Our results provide a descriptive account of the relationship between hate speech by politicians and other users, and shows that right-wing political figures and supporters are strong emissors of hate speech, while women, especially those from the left-wing are more prone to receive violent content.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".