Standardizing and disseminating knowledge: the role of the OECD in global governance
Bibliographic record
Abstract
If ‘knowledge is power’, it is unsurprising that the production, legitimation, and application of social scientific knowledge, not least that which was designed to harness social organization to economic growth, is a potentially contentious process. Coping with, adapting to, or attempting to shape globalization has emerged as a central concern of policy-makers who are, therefore, interested in knowledge to assist their managerial activities. Thus, an organization that can create, synthesize, legitimate, and disseminate useful knowledge can play a significant role in the emerging global governance system. The OECD operates as one important site for the construction, standardization, and dissemination of transnational policy ideas. OECD staff conducts research and produces a range of background studies and reports, drawing on disciplinary knowledge (typically economics) supplemented by their ‘organizational discourses’. This paper probes the contested nature of knowledge production and attempts to evaluate the impact of the OECD’s efforts to produce globally applicable policy advice. Particular attention is paid to important initiatives in the labour market and social policy fields – the Jobs Study and Babies and Bosses.
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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 | 0.043 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".