Feminist Identity and Online Activism in Four Countries From 2019 to 2023
Bibliographic record
Abstract
The COVID-19 pandemic heightened burdens on caregivers, but also the visibility of caregiving inequalities. These grievances may activate a feminist identity which in turn leads to greater civic and political participation. During a pandemic, online forms of participation are particularly attractive as they require less effort than offline forms of participation and pose less health risks compared to collective forms of offline activism. Using survey data from four countries (Canada, France, the United States, and the United Kingdom) collected in 2019 (prior to the pandemic), 2021 (during the pandemic), and 2023 (post-pandemic), we examine the relationship between self-identifying as a feminist and signing online petitions ( n = 18,362). Our multivariate analyses show that having a feminist identity is positively related to signing online petitions. We consider the differential effects of this identity on participation for men, women, non-binary people; caregivers versus non-caregivers; and respondents in different countries with varying levels of restrictions due to the pandemic. A feminist identity is more important for mobilizing caregivers than non-caregivers, whether or not the caregiver is a man or a woman. While grievance theory suggests differential effects by country and time period, we find a consistent role of feminist identity in predicting the signing of online petitions across time and across countries. These findings offer insights into how different groups in varying contexts are mobilized to participate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".