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Record W4224757216 · doi:10.1002/epa2.1142

Analyzing national policy styles empirically using the Sustainable Governance Indicators (SGI): insights into long‐term patterns of policy‐making

2022· article· en· W4224757216 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

VenueEuropean Policy Analysis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsConceptualizationOperationalizationCorporate governanceSet (abstract data type)Political sciencePolicy analysisPublic economicsEconomicsPublic administrationManagementComputer science

Abstract

fetched live from OpenAlex

Abstract The concept of national policy styles differs between states in respect of whether their governments react to policy changes in an anticipatory or reactive fashion and whether they seek to achieve consensus with societal actors or impose decisions on them. To date, this conceptualization has been applied to a limited number of states and produced only a small set of case studies due to the absence of large‐n data. We assess whether the dimensions on strategic planning and public consultation of the Bertelsmann Sustainable Governance Indicators (SGI) provide conceptually sound and empirically insightful indicators of national policy styles. Our explorative analysis reveals that the SGI are useful for operationalizing the concept of national policy styles and could advance the debate on it. Our analysis shows that differences exist between countries in terms of their policy styles, and that the policy styles remained stable in most countries between 2014 and 2020.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.012
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.022
GPT teacher head0.354
Teacher spread0.332 · 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