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Record W2837258019 · doi:10.1080/17516234.2018.1493768

Framework or metaphor? Analysing the status of policy learning in the policy sciences

2018· article· en· W2837258019 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

VenueJournal of Asian Public Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMetaphorCLARITYAgency (philosophy)Field (mathematics)Subject (documents)VocabularyPolicy analysisSociologyEpistemologyData sciencePolitical scienceComputer scienceSocial scienceLinguisticsWorld Wide WebPublic administration

Abstract

fetched live from OpenAlex

Recently, it has been argued that the many works on policy learning constitute a stand-alone basis for understanding policy processes. In this study, we evaluate this claim through a bibliometric analysis of 588 publications on the topic in the Web of Science database, complemented by a literature review. We find that while the study of learning is supported by an active and growing research community, it has neither definitional clarity nor a shared vocabulary. And, further, its model of agency is both incomplete and inconsistent. As such, the subject remains more a metaphor than a framework of analysis, per se, and has little potential to advance epistemologically. Given this analysis, we argue that intellectual resources are better spent organising research on learning within existing frameworks rather than attempting to create a new stand-alone one that would contribute to the further splintering of an already fragmented field of study.

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)0.007
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.427
Teacher spread0.356 · 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