The Intersectionality of Twitter Responses to Black Canadian Politicians
Bibliographic record
Abstract
Research has shown that Black politicians in the Global North contend with higher instances of abusive language on social media platforms. The study investigates how public interactions engage with the intersectional positionalities of nine Black Canadian politicians. We collected all the replies to tweets posted by the politicians from 2006 to 2021. Results from the manual analysis showed that 56% of the tweets had a neutral tone, meaning that even if they contained abusive language, they did not directly address the politician. They were also not complimentary. There were more negative tweets than positive ones; 23% versus 21%. The themes of the tweets with negative tones centered on opposition to the politicians’ discussion of racial inequalities or racism, or their party affiliations, especially affiliation to the Liberal Party or relationship with Prime Minister Trudeau. The manual analysis showed women politicians received higher rates of abuses, while in the sentiment analysis stage that covered the entire data set, men were more trolled with 66.6% of words directed at them being negative, compared to 55.7% for the women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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".