Bibliographic record
Abstract
Introduction International efforts to support a universal right to education have been a ubiquitous part of international society over the past five decades. Today it would be difficult to find any meeting of world leaders in which the universal right to education is not trumpeted as an international goal. Yet despite the engagement of a variety of global governors in “education for all” (EFA) efforts, a wide gulf has historically divided global EFA aspirations and achievements. This chapter looks at the history of global governors and their “education for all” initiatives, focusing in particular on the changing relational dynamics among EFA governors. Over the past six decades, EFA has become a prime venue for displaying commitments to equity, economic redistribution, and human rights – attracting an expanding cast of governors precisely because it can enhance their legitimacy and authority. Yet ironically, the growth in the number of EFA governors has led to competition and fragmentation in international EFA activities. EFA's global governors have deployed competing technical repertoires, been guided by strikingly different bureaucratic and geopolitical interests, and have drawn on different sources for their authority. The result has been a system-wide form of “organized hypocrisy,” in which global governors repeatedly set wide-ranging international targets and goals, for which neither global governors nor developing country states are held responsible (Barnett and Finnemore 2004).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".