Refugee policy narratives of political parties in Turkey
Bibliographic record
Abstract
This study examines the diverging narratives of the governing and opposition parties in Turkey about Syrian refugee policies between 2011 and 2023. We use a mixed method combining process tracing and quantitative exploratory text analysis of 1,001 parliamentary group speeches by four political parties. Our analysis reveals six main narratives about Syrian refugees: temporariness, fraternity, civilizationist humanitarianism, rights-based humanitarianism, burden, and repatriation. The ruling party, AKP, embraced the pro-refugee policies by mixing the first three narratives until 2017, after which the repatriation narrative gained significance. The CHP, the main opposition party, codified a burden narrative, which problematised Syrians as a threat to border security, national economic resources, and social cohesion. Similarly, the Turkish nationalist MHP adopted the burden narrative until its alliance with the government. After the 2019 local election, all three parties’ narratives slightly converged around the repatriation narrative. One exception to narrative convergence(s) among parties is the pro-Kurdish party HDP, which consistently emphasises rights-based humanitarianism. Our findings provide insights about how political parties develop, contest, revise, and converge their narratives about refugees over time. This contributes to the de-centering on political narratives and migration governance by bringing in a non-Western perspective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 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".