National transparency: Global trends and national variations
Bibliographic record
Abstract
Nation-states worldwide are institutionalizing a culture of transparency and accountability. In our analyses of data reported in the United Nations (UN) Statistical Yearbooks since 1970, we identify two main trends: (1) national governments are providing a greater amount of data on a larger set of social, political, and economic domains, and (2) national governments increasingly offer such data in accordance with international standards introduced by the UN. In addition, we find that the overall cross-national trend toward transparency and accountability, as measured by the standard reporting of national accounts to the UN from 1970 to 2000, is driven by a unique set of factors in each time period. Specifically, domestic and economic conditions drove the trend toward transparency before 1990, whereas political factors have driven transparency since then. Throughout the period studied, the presence of links between a given country and world society has increased the likelihood that it will engage in transparent reporting. We conclude that active networking with international governmental organizations, such as the UN, teaches governments the norm of transparency, inculcating them with the rationales of public accountability and proper governance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".