The Good Governance Quandary: The Elusive Search for Role Models
Bibliographic record
Abstract
Abstract A substantial consensus has emerged in development circles that the reason why some countries are rich and others poor is largely a reflection of the quality of their institutions – political, bureaucratic, and legal – and that countries with seriously dysfunctional institutions cannot expect to pursue a successful long-term trajectory of economic and social development. Many studies support this consensus, but institutional reform efforts for developed countries have resulted in mixed to weak results; many of these efforts have failed, for example, to establish a robust rule of law to protect the rights of citizens, publicly accountable political regimes, a meritocratic, noncorrupt, and efficient bureaucracy, and an independent media. Reportedly up to 60% of donor-assisted reforms have yielded no measurable increase in government effectiveness. It is inferred from this disappointing result that institutional transplants are often ineffective, and the path dependence, caused by accretions of the particularities of given countries’ histories, cultures, politics, ethnic and religious make-up, and geography leaves each country, for the most part, “to write its own history”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".