Feminist Communities of Practice: Building Stronger Research Coalitions to Counter Antagonism in the Academy
Bibliographic record
Abstract
In the face of academic antagonism, this article argues that feminists must build caring and careful communities of practice to sustain ourselves. In it we explore the importance of creating intentional, feminist-oriented spaces of actionable solidarity to protect activists and academics from institutional antagonisms. Through our encounters as researchers working within feminist archives, and as academics who are part of feminist research collectives, we have developed a process for creating provisional solidarities that protect both our persons and our activisms. This process is based on our lived experiences, archival explorations, and social media research that informs our sometimes messy and often nuanced praxes of community-building as a counterpoint to increasing neo-liberal co-option of discourses of solidarity. In this paper we provide some guideposts for how we do our work as a way of inviting further conversation around how to manage the antagonisms we face as feminist scholars.
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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 | 0.043 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".