MétaCan
Menu
Back to cohort
Record W4382699881 · doi:10.33137/cpep.v1i1.40255

The OECD's Education Agenda: Balancing Limited Membership, Global Reach, and Domestic Interests in Latin America

2023· article· en· W4382699881 on OpenAlexaff
Tatiana Britto

Bibliographic record

VenueCritical Perspectives in Education & Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLatin AmericansGlobeGlobalizationCorporate governancePolitical scienceSoft powerAppealPoliticsNorm (philosophy)Higher education policyGlobal governancePower (physics)Higher educationEducation policyPolitical economyEconomic growthSociologyEconomicsManagement

Abstract

fetched live from OpenAlex

Global dynamics are becoming more prominent in education policy discussions, with the OECD standing out as a major player in the global education policy arena. Despite its selective and limited membership, the OECD’s reach and influence extend across the globe. This paper explores this paradox through an analytical review that bridges the field of education policy to the broader panorama of global governance, examining factors related to the growing involvement of Latin American countries with the OECD’s education agenda. Delineating the organization’s history and expansion strategy, as well as domestic drivers for membership and participation, this article examines the complex interplay between globalization, international mechanisms of norm diffusion and soft power, national interests, and internal politics. It concludes by outlining the multidimensional game that links institutional reasons, international dynamics, and domestic motivations with the appeal of organizations such as the OECD and its education initiatives in the Latin American region.

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)0.010
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 score0.018
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0050.013
Scholarly communication0.0130.007
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.457
Teacher spread0.414 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCritical Perspectives in Education & PolicySame topicGlobal Education and MulticulturalismFrench-language works237,207