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Record W4309831373 · doi:10.34069/ai/2022.56.08.30

The state of kurdish language through public policies in Turkey after 1980

2022· article· en· W4309831373 on OpenAlex
Recep Bilgin, Seydali Ekici, Fatih Sezgin

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista Amazonia Investiga · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsGlenbow Museum
Fundersnot available
KeywordsRealmTurkishState (computer science)Language policyPolitical scienceAdventurePublic administrationLawSociologyLinguisticsComputer science

Abstract

fetched live from OpenAlex

Making of public policies is a process through which the state determines relevant topics for the sake of its citizens and implements them. In Turkey, the public policies about Kurdish language after 1980 are outstanding such that the actors of coup d’état eagerly embraced the former idea of nation state which theoretically requires one common language in the borders of the given country. In parallel with this notion, the soldiers, who are the impeccable followers of Kemalist idea, put much pressure on local languages in Turkey, especially Kurdish language. They made some laws and forbade other languages than Turkish. But later, the conservative governments reigned in Turkey which had different ideas about this topic. As they got the opportunities, they made use of them so as to improve the situation for that language. The conservative governments handled this topic in the realm of freedom and human rights and created different public policies, so the adventure of Kurdish Language followed a much different track under different governments. This is a qualitative study, and the data were compiled from the related literature and evaluated accordingly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.299
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it