The <i>state effect</i> in contemporary Turkey: tracing ‘honour’ as a trope of gendered victimhood in cases of sexual assault
Bibliographic record
Abstract
This article explores the implementation of the sexual assault law in the wake of the new Turkish Penal Code enacted in 2005. Through a multi-method qualitative approach, the article examines the institutional logics and gendered scripts through which the state determines which institutions can provide evidence to courts and how victimhood is understood in sexual assault cases. First, I focus on the priority granted to the Institution of Forensic Medicine for preparing forensic reports. Drawing on Mitchell’s ‘state effect’, I argue that this move reflects the Turkish state’s efforts to reconstitute itself as a rational, scientific, and impartial entity amid profound political and legal transformation in the country. Second, I examine the statements made within the judiciary regarding cases of sexual assault against women. Observing the ongoing construction of sexual violence primarily as an attack against women’s ‘chastity’, I further argue that ‘honour’ remains a powerful point of reference in the legal discourse and practice, sustaining the moralistic attitude in the legal treatment of sexual offences. Finally, I maintain that the state’s exclusive claim to rationality, scientificity, and impartiality regarding women’s experiences of sexual assault lays bare the gendered dimension of the production of the ‘state effect’ in contemporary Turkey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".