Central-Local Relations in Asia-Pacific: Convergence or Divergence?
Bibliographic record
Abstract
List of Tables List of Figures List of Appendices Preface Notes on Contributors Central-Local Relations: Getting the Right Balance M.Turner Central-Local Relations and Responsibilities in Bangladesh Experiments with the Organization, Management and Delivery of Services D.Hulme & N.A.Siddiquee Beyond Integration? The Need to Decentralize Central-Regional/Local Relations in Indonesia R.Gerritsen & S.Situmorang Central-Local Relations in Thailand: Bureaucratic Centralism and Democratization W.Wongsekiarttirat Philippines: From Centralism to Localism M.Turner Decentralization in Papua New Guinea: Two Steps Forward, One Step Back R.J.May Scale and Governance in the South Pacific P.Larmour Rethinking the Philosophy of Central-Local Relations in Post-Central Planning Vietnam T.Vasavakul Central-Local Relations in the Lao People's Democratic Republic: Historic Overview, Current Situation and Trends P.Keuleers and L.Sibounheuang Conclusion: Learning from the Case-Studies M.Turner Index
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".