Bibliographic record
Abstract
Fear is an emotion we generally try to avoid. It is associated with reflexes such as immobility, flight or, in the case of activism, demobilization. This article partially questions this received wisdom based on empirical research with feminists in Quebec (Canada) and Romandie (Switzerland). This article suggests that fear can also sometimes be a ‘drive to action’ for feminists and, at other times, rein in their activism. It first examines the varying effects of fear on feminist activism and links these to the different positioning of feminists within social relations of race, class and sexuality. Second, it suggests that the origin of the fear, its interactions with other emotions, and the emotional work performed by interviewees were other key factors shaping the impact of fear on their activism. In other words, we will discuss four main emotional sequences which take different directions depending on whether the fear stems from police violence, male violence, fear for one’s reputation or fear of exclusion. We will also look at the effects they give rise to: protective mechanisms, censorship, deepening knowledge and partial withdrawal from majority feminist circles. The article further addresses the fear of male violence which pushes many to become involved in the feminist movement. Combined with anger and emotional work to reduce its intensity, fear acts as a true driving force for action. Additionally, the article shows that factors which impede feminist activism are mostly related to a minority position in the movement and repeated anti-feminist threats.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".