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Record W7096801652

The Role of the OECD in the Orchestration of Global Knowledge Networks”, paper presented at Canadian Political Science Association annual meetings

2007· article· en· W7096801652 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceOrchestrationAssociation (psychology)PoliticsState (computer science)Knowledge production
DOInot available

Abstract

fetched live from OpenAlex

The Organization for Economic Cooperation and Development has not received nearly the scholarly attention one might expect given its size and budget. It presents a puzzle since states give it considerable support and yet many of the problems it addresses, like education policy, do not involve the interdependence that characterizes most issues involving collaboration among states, and its relative lack of measurable knowledge outputs make it unlikely that state support is due to rational cost-benefit calculations. The paper provides a constructivist analysis of the OECD, arguing that the organization’s identity-enhancing role and the way that it produces social facts that are taken for granted and, due to their links to identity, self-evidently of value, are more important than rational calculation in explaining the OECD’s existence and effects. These arguments are assessed through an examination of two areas in which it has been active: peer review and corporate governance. The Organization for Economic Cooperation and Development is commonly perceived as simply a highly technical research organization with little significance for world politics. This underestimates the importance for global governance of the knowledge networks that the OECD helps orchestrate, and the contribution of the OECD’s knowledge production to the identities of

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.304
Teacher spread0.299 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2007
Admission routes1
Has abstractyes

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