Repressing in the name of? Externalization dynamics in Turkey’s use of digital repression against refugees
Bibliographic record
Abstract
Over the last decades, Turkey has expanded its digital capabilities in various issue areas. At the same time, regime change under the Justice and Development Party has resulted in unprecedented state repression against various groups, which increasingly occurs via digitized channels. While Turkey has been building digital capabilities since the late 1990s, efforts to control the flow of refugees since 2015/16 have further resulted in the accumulation of such capabilities. Turkey’s partners, most notably the EU, have been pivotal in Turkey’s development in this sphere. We trace Turkey’s deployment of its newly gained digital repressive infrastructure and triangulate insights from open-source data (i.e. government data, newspaper reports, and other digital traces) to map processes of (mis)use. We argue that the AKP regime is not only deploying digital and AI technologies for the purpose of border and migration governance, but it is also misusing these technologies by engaging in digital repression against refugees. We further find that digital repression strategies employed against refugee populations largely overlap with strategies used to gain control over political opposition and civil society actors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".