Bibliographic record
Abstract
Corruption, or misuse of public office for private gain, is continually in the headlines. From hunger strikes in India to scandals around defence procurement in Canada, attention to corruption is growing. Corruption has been linked to weak economic growth and development outcomes (Kulshreshtha, 2008, p. 558). Though the problems of corruption have led aid agencies to recognize the fundamental importance of good governance, progress has been halting. In fact, there are almost no cases of a developing (‘post-colonial’) country moving from a highly corrupt situation to one in which corruption is minimized. Civil service reforms and elections of pro-reform candidates seem futile to bring long-lasting results in countries as diverse as India and Argentina. Failing states from Afghanistan to Iraq reveal corruption to be a central issue. While the role of culture, often defined as shared beliefs, attitudes, values, norms and practices is recognized in the academic literature as an element of potential importance, to date it has not been incorporated into the design of aid programmes to reform civil services. In this collection, we examine why attempts to reform the civil services of developing countries have largely failed in good part because they focus on the formal and ignore the need to reform culture as well. Our case studies including Singapore, Hong Kong, Chile, Afghanistan, Swaziland, India and Nigeria span a wide range of failures as well as a few success stories and are based on strong author knowledge of the local context as well as field research.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".