The Role of the OECD in the Orchestration of Global Knowledge Networks”, paper presented at Canadian Political Science Association annual meetings
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".