Repressing in the name of? Externalization dynamics in Turkey’s use of digital repression against refugees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it