‘It definitely felt very white’: race, gender, and the performative politics of assembly at the Women’s March in Victoria, British Columbia
Bibliographic record
Abstract
This article reflects upon the challenges of building solidarity across the racial divide in the struggle for women’s rights as displayed at the Women’s March on Washington and the ‘sister’ marches in cities across the United States and beyond. In particular, we highlight the concerns that women of color raised regarding the ‘whiteness’ of the marches and the lack of reciprocity that they often experience when participating in interracial coalitions with white ‘allies.’ Drawing upon Judith Butler’s recent work on the performative politics of assembly and Chantal Mouffe’s conception of radical democracy, we argue that concerted bodily action and the enactment of collective political subjectivities are contested processes in which ‘bodies-in-alliance’ may march together but do not necessarily act in conformity. It is therefore crucial to cultivate agonistic spaces within solidarity movements in which adversarial conflicts among ‘allies’ can emerge if such movements are to remain committed to radical democratic politics. We explore these issues further by discussing our own conflicting experiences at the rally to support the Women’s March in Victoria, British Columbia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.017 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".