Bibliographic record
Abstract
Failure of bureaucracy has prompted many efforts at reforming it. But administrative reform has failed in many developing countries, including India, for a variety of reasons. The costs of the bureaucracy’s malfunctioning are huge. Any attempt to recharge the Indian bureaucracy would need an examination of its design flaws. The first design flaw is a merit system that does not select for needed administrative capabilities. Second, short uncertain terms of members of the elite services. Third, overloading and centralization. Fourth, a monolithic state. Successful recharging of administration in Britain, Canada, Malaysia, Singapore, New Zealand, etc. indicate that a large part of the state needs to be broken up into semi-autonomous executive agencies. These need to have competitively selected heads on fixed tenures who operate autonomously within the constraints of an MoU with the government. The process adopted in Britain to set up and run executive agencies is described, and example of Passport Agency is given to illustrate how a government body may get transformed after its conversion into an executive agency. The contrasting performance after liberalization of India’s central government public enterprises, whose management structure resembles executive agencies, and the states-owned public enterprises with politician chairpersons and IAS managing directors on short, uncertain tenures supports fragmentation of the bulk of the Indian state into executive agencies for revitalizing administration.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".