‘I’ve grown fearful of any rustle behind me’: defining anticipating discriminatory violence <i>as</i> violence
Bibliographic record
Abstract
Marginalised people fear and expect violence, often daily. This prompts us to ask, is anticipating violence a violence in and of itself? Asking and answering this question extends the feminist, critical race, violence and trauma studies project of broadening traditional understandings of violence to name ignored forms of violence as violence (e.g. epistemic or representational violence). Ultimately, we argue that anticipating discriminatory violence is violence in and of itself. To do so, first we contest the common assumption that violence is intentional. The idea that violence needs to be intentional is a long-held myth that functions to deny various forms of violence. Second, we challenge the idea that violence requires a clear perpetrator. Systems of oppression and discriminatory ideologies enact violence, but there often is no clear perpetrator. When we are preoccupied with claiming that violence involves an intentional actor, we neglect to attend to the ways in which oppressive ideologies and systems structure marginalised people's daily lives and experiences of (anticipating) violence. Living under the Western capitalist cisheteropatriarchal regime renders the ‘everyday’ a site of trauma and violence. This framework for reconceptualising what ‘counts’ as violence creates space to move beyond violence in its most traditional forms: the punch, the slur. Anticipating violence is the logical consequence of living under systems of oppression. When a group of marginalised people collectively anticipate violence, it is clear violence has already happened and is happening all around us: we posit that our conceptualisation of anticipating violence as violence is not intended to validate all forms of anticipated violence.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".