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Record W2806437523 · doi:10.1590/0034-761220170020

Diffusion of CCTs from Latin America to Asia: the Philippine 4Ps case

2018· article· en· W2806437523 on OpenAlexaff
Michael Howlett, M. Ramesh, Kidjie Saguin

Bibliographic record

VenueRevista de Administração Pública · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSimon Fraser University
FundersLee Kuan Yew School of Public Policy, National University of SingaporeNational University of Singapore
KeywordsPolicy transferArticulation (sociology)Latin AmericansConditional cash transferDiffusionPolitical scienceTRACE (psycholinguistics)PhenomenonSouth asiaSubject (documents)SociologyPublic administrationEpistemologyComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract The purpose of this study is to understand the role of international and domestic actors, ideas and processes in the diffusion of public policies. It argues that existing studies on the subject do not provide an adequate explanation of the mechanisms through which diffusion takes place, nor do they sufficiently address the roles of actors affecting the policy transfer process. We address these shortcomings by studying the diffusion of conditional cash transfer (CCT) programs from Brazil and Mexico to the Philippines over the past decade. We use the concept of an ‘instrument constituency’ to delineate and trace the various actors and channels involved in the diffusion of CCTs. The case study shows that these groups of actors dedicated to the articulation, adoption and expansion of particular policy instruments are central players in transnational diffusion of policies and offer a robust explanation of the phenomenon.

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)0.002
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.039
GPT teacher head0.358
Teacher spread0.319 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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