Transitional dynamics and the evolution of information transparency: a global analysis
Why this work is in the frame
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Bibliographic record
Abstract
The last quarter of the 20th century was a period of sustained economic growth across many countries. Countries' institutional arrangements have been commonly employed as factors in the convergence studies of economic growth and income levels. However, the issue of whether institutions themselves converge has been under-researched. Using the nonparametric distribution dynamics approach and a sample of 194 countries during the 1980-2010 period, we examine a tendency for countries' informational transparency (IT) to converge over time. We find that whilst there is some evidence of unconditional convergence across countries, there is stronger evidence for convergence clubs to emerge, at both regional and income levels. Notably, the level of IT of the low-and lowermiddle-income countries and those situated in Africa, and Middle East regions tend to converge towards a level significantly below the global average. We also find a strong relationship between income and IT.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it