Intellectual crimes and serious violation of human rights in Turkey: a narrative inquiry
Bibliographic record
Abstract
After the crackdown following the failed coup attempt of 15 July 2016, the Turkish government persecuted hundreds of thousands of people, and much damage has continued to afflict Turkish civil, legal and political life. Many intellectuals have been arrested, tortured, victimised, jailed, and have been subjected to practices that contravene international law. Some of them were able to flee the country and settled in Europe, Canada, and the United States. This study used a narrative synthesis of qualitative design. By analysing the critical life stories of those who have fled, this article provides a narrative, in-depth exploration of their experiences in the Turkish turmoil. A purposive sample of 15 Turkish intellectuals was utilised, and the data were collected through semi-structured interviews from December 2018 to June 2019. Two main themes emerged: torture and right to a fair trial. The results of this study showed the depths of the persecution, torture, and humiliation that the men, women, and children who were detained and jailed endured. The results also showed the most violated human rights were torture, some of which resulted in deaths in custody and the absence of a fair trial.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".