Bibliographic record
Abstract
Global research consistently demonstrates that despite targeted efforts, for historically marginalised and diverse women, the forms, types, and frequency of violence that they experience remain largely unchanged and, in some instances, have only become more intensified and prevalent. In this chapter, we created composite stories by drawing upon media narratives of violence against disabled women, BIPOC women, and sexually and gender-diverse women to reveal the continual stigmatisation of bodies and minds deemed outside the boundaries, borders, and polity of settler colonial nation states—Canada and Australia. By articulating the co-constitution of settler colonialism, gendered violence, and disability, we trace how these violent processes of elimination and exploitation are gendered and gendering, both creating impairments and biopolitical meanings of disability to enable the settler colonial management of different groups. We argue that stigmatisation is a mechanism of settler colonial biopolitical power operating as structures of disablement, which in turn entrenches stigmatised women in conditions in which gendered violence is likely and normalised. Importantly, the stigmatisation process of disablement is one of political struggle; it therefore informs the possibilities of broad-based coalitions against gendered violence that can be mobilised across different gendered and diverse women within and across settler colonial nation states.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".