Gendered and Racialized Online Incivility in Four Dimensions
Bibliographic record
Abstract
Candidates for political office frequently face online incivility and abuse. Research consistently finds that incivility and abuse can hinder the full political participation for women, and especially racialized women. However, there are competing theories and contradictory findings about precisely how gender and race shape online incivility. Much research focuses on how gender and race affect the volume of uncivil messages. This chapter extends that work to highlight other crucial dimensions of online incivility. We propose a framework to encompass all relevant characteristics of online incivility, which include: (1) frequency of incivility, (2) qualitative forms, (3) targeting and distribution, and (4) interaction with offline discrimination and threats. This emerged from an analysis of 969,308 tweets from the 2019 Canadian election and in-depth interviews with 31 candidates and campaign staff. We show that while candidates’ gender and race do not necessarily predict the volume of online incivility, women and racialized candidates do face more misogynist and racialized incivility. We also find some misogynist and racist content is directed at prominent male and/or white candidates’ accounts in apparent efforts to disseminate these discourses more broadly. Finally, given the realities of online abuse in Canada, this chapter suggests how to better support candidates and politicians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".