Politicizing Disability/Disablement: A Case of Hunger-Strike/Death-Fast by a Kurdish Political Prisoner in Turkey
Bibliographic record
Abstract
Over the past several years, we have been engaged in human-rights/disability-rights activism and organizing with disabled and/or traumatized survivors who have been victims of the state violence (e.g., war, incarceration, forced migration). As we interviewed them, documented their narratives, and/or read their prison memoirs, we re-lived our own past experiences with state violence as two racialized Middle Eastern women who have experienced incarceration in Turkey and Iran. Given our experiences, and being Disability Studies (DS) scholars and feminists, we have developed a new model to approach disability and disablement in the global southern/“third world” contexts. Herein, we narrate a real-life story of incarceration, torture, and hunger strike that has resulted in permanent disability. Following that we introduce our Transnational Disability theory to set the foundation for a historical and dialectical materialist understanding (DHM) of disablement. Finally, we apply the new model to our interviewee's narrative and analyze it through a DS lens. We end the paper by offering future directions for DS and Ethnic Studies scholars, prison abolitionists, and/or disability-rights activists who are enthusiastic about using the transnational disability approach in theory and political praxis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".