Proving injustice: Smuggler killings, impunity work, and vernacular counterforensics in Turkey's Kurdish borderlands
Bibliographic record
Abstract
Abstract Kurdish smugglers have been targeted and killed by security forces in Turkey's Van borderlands systematically and with impunity. In response, the killed smugglers’ families and their lawyers conducted what I call vernacular counterforensics —the forensic examination both of the killings and of the legal authorities’ failure to investigate them properly. Associating the Kurdish borderlands with terrorism, the legal authorities often avoided collecting evidence on the killings to make potential perpetrators remain unknown or legally authorize the killings. By documenting this impunity work through their counterforensics, Kurdish complainants and lawyers demonstrated the judiciary's complicity in the systemization of state anti‐Kurdish violence. While anthropological studies show that criminal law operates by individualizing violation claims and perpetrators, vernacular counterforensics illustrates a distinct use of criminal law that reveals, rather than blurs, the state crimes’ systematic‐collective aspects. Rather than differentiating technoscientifically produced crime scene evidence from the political circumstances of state crimes, Kurdish complainants and their lawyers used the selective production of such evidence to corroborate the killings’ unlawfulness and their systematic‐collective character. This dual use of forensic evidence permits us to rethink analytical and methodological premises that view forensic evidence as fully verifiable and universally applicable and contrast it against contextual and contingent knowledge forms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".