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Record W4389737632 · doi:10.1017/ics.2023.11

Scales of ideational policy influence: A multi-level, actor-centric, and institutionalist perspective on the role of ideas in African social policy

2023· article· en· W4389737632 on OpenAlexafffund
Daniel Béland, Rosina Foli, Privilege Haang’andu

Bibliographic record

VenueJournal of International and Comparative Social Policy · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsCanadian Institutes of Health ResearchMcGill University
FundersUniversity of TorontoUniversity of Oxford
KeywordsPerspective (graphical)SociologyPovertyPolitical scienceEconomic geographyScale (ratio)Positive economicsEconomic systemPolitical economyEconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Abstract Although there is a growing literature on transnational ideational processes in sub-Saharan Africa, the linkages between local, national, and transnational actors and ideas in African social policy would gain from more systematic mapping. In this paper, we explore what we call the “scales of ideational policy influence” by sketching a multi-level, actor-centric, and institutionalist perspective on ideational policy influence at the local, national, and transnational scales. This discussion leads to analysis of how these scales interact in terms of specific ideas and how both governmental and non-governmental actors seek to impact social policy decisions in sub-Saharan Africa. To illustrate the three scales of ideational influence and their interaction, the paper turns to the making of poverty reduction policies in Ghana. We show how policy ideas move from the global level to a national and subnational level using ideational mechanisms aided by the institutional position of actors and material factors.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0030.037
Scholarly communication0.0120.009
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.113
GPT teacher head0.366
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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