Transparency and Its Discontents: How IO Transparency Influences Domestic Resistance to Government Reforms
Bibliographic record
Abstract
How do international organizations influence domestic transparency? Studies\ntypically contend that in order to be instrumental in promoting good government\ninstitutions, international organizations have to embody these norms in their own\nwork. International organizations (IOs) have recently implemented a number of\nreforms to open up certain official documents and proceedings to public access. These\nreforms are generally expected to promote support for transparency in member\ncountries. We suggest that one important and overlooked condition determines the\nability of international organizations to meet these expectations: the quality of IO\ndecision making, defined as its effectiveness, predictability and fairness. The paper\ndevelops these ideas theoretically and presents a study on how these reforms influence\nperceptions of the merits and drawbacks of transparency among senior government\nofficials in environmental ministries, involved in projects seeking finance through the\nClean Development Mechanisms and the Multilateral Fund for the Implementation of\nthe Montreal Protocol.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".