A quarter century of online discussions on Arabic and Kurdish in Turkey: a comparative analysis of language attitudes and controversies
Bibliographic record
Abstract
Abstract Over the past two decades, Turkey has introduced reforms to enhance the linguistic rights of its two most widely spoken minority languages, Kurdish and Arabic, marking a departure from its historically monolingual policies. Violations of linguistic rights and continued shifts toward Turkish continue, though, explained by previous research as resulting from poor policy implementation. Language attitudes and ideologies at the grassroots level also play a critical role in the effectiveness of language policies, however, but these remain largely overlooked both in research and policymaking. This study therefore systematically analyzes 2,075 topic titles and 10,000 individual comments posted about Kurdish and Arabic on a popular Turkish online forum between 1999 and 2024, revealing a pervasive ideology of normative monolingualism and widespread negativity toward both languages, despite the reforms that were introduced. Kurdish receives a more positive reception than Arabic, but its use is still considered controversial, particularly in political and educational contexts. Because Arabic is often linked to political Islam and Syrian refugees, it is viewed quite negatively. The study thus shows that well-intentioned language policies still have to be implemented in actual contexts, and that grassroots attitudes and ideologies may contribute to thwarting their effectiveness. Policymakers wishing to increase the success of their language policies may have to work on creating a favorable reception of these policies, not least in major online social spaces.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".