Diverse Political Women in Canada and Online Attacks: Experiences, Perspectives, and Insights
Bibliographic record
Abstract
Multiple academic disciplines agree upon the importance of women having diverse representation in politics because their unique perspectives are strongly thought to have positive implications on policies that affect the health and well-being of all people (Clayton & Zetterberg, 2018). Yet, abuse or harassment levied at these women both online and offline likely diminishes their voices or desires to remain politically engaged. Using hermeneutic phenomenology and intersectional feminism, I explored the experiences of a diverse group of political women in Canada with online attacks to gain their insights on potential implications and strategies for change. The findings analyze the participants’ complex relationships with social media, the unique challenges of each platform, the interlock of online and offline harassment, the women’s resiliencies and strategies to cope with online attacks, and their ideas for potential resolutions. The implications for critical social work practice are identified as: insight on the continued need to challenge Eurocentric heteropatriarchal colonial institutions, online and offline; the importance for Canadian social workers to (re)imagine their roles in online spaces; and, the need to develop methods with interdisciplinary teams to combat ideological radicalization on online echo chambers. Regarding future research, there exists much potential. One focus I recommend is that an interdisciplinary team of people, representing various socio-political positionalities, study how to perform community building projects on online echo chambers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.055 | 0.020 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".