Intersectionality and the gendered discussion around Muslim Canadian politicians on Twitter
Bibliographic record
Abstract
Abstract This study investigates users’ gendered attitudes towards Muslim Canadian politicians on Twitter with regard to intersectionality. Its purpose is to understand the tone and intersectional dimensions of Twitter users’ responses to Muslim Canadian politicians and the gendered responses to them. Therefore, we extracted all the available Twitter replies to 11 Muslim men and women politicians. Using a mixed method approach, we investigated how the public engages with Muslim politicians by focusing on intersectional characteristics. Results show that Muslim politicians are not directly under attack because of their religion unless they engage in public discussion of Islamic issues. Overall, both men and women politicians received higher numbers of negative replies than positive ones. Women received more personal replies while men received more professional ones. For both men and women politicians, personal attributes such as nationality, gender, and religion were used as a means for discriminating against them. However, we found that replies to women were more likely to be stereotypical and refer to characteristics of their identity and their appearance. The digital analysis shows, however, that men politicians were more trolled than their women counterparts and that the quality of attacks differed as well.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".