MétaCan
Menu
Back to cohort
Record W1974171287 · doi:10.1177/1468018113499575

Translating travelling ideas: The introduction of unemployment insurance in Turkey

2013· article· en· W1974171287 on OpenAlex

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

VenueGlobal Social Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsCarleton University
FundersWorld Bank Group
KeywordsOperationalizationUnemploymentInstitutionalisationPoliticsPolicy transferProcess (computing)Political scienceSociologyPolitical economyPublic administrationEconomicsEconomic growthEpistemologyLawComputer science

Abstract

fetched live from OpenAlex

The policy transfer/learning framework provides clues about the ideational sources of new institutional components such as the new unemployment insurance (UI) programme in Turkey. Yet it is not clear how ideas produced by transnational actors within different networks are conveyed to the national political landscape by transnational and/or domestic actors, and how these ‘travelling ideas’ are then operationalized at the national level. How do international organizations (IOs) get involved in the translation of travelling ideas, such as those which informed the design of Turkey’s UI scheme, into the national landscape? How did the domestic actors articulate and modify the ideas of IOs during the institutionalization process of Turkey’s UI programme? This article suggests that the ‘translation’ concept can provide further insights into better understanding the interaction between IOs and domestic actors that occurred during the introduction process of the UI scheme in Turkey.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.335
Teacher spread0.313 · 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