Understanding the Deep Roots of Success in Effective Civil Services
Bibliographic record
Abstract
As discussed in the preface to this edition, there are three cases in the developing world which stand out in regard to corruption: Singapore, Hong Kong, and Chile. While all have had differing rates of economic growth and their own particular struggles with governance, they are the only consistently high performers over long periods of time. To better understand the roots of their success, this article compares the three cases with two other cases with perennial issues of corruption, Nigeria and Paraguay. Our analysis is organized around three basic categories. The first is to examine the personnel systems of each civil service and to see how reforms in recruitment and promotion—the main focus of aid agencies—have reduced corruption. The second is to examine sanctions for corruption in order to understand how they become real rather than just rhetorical. Civil service reform efforts so far have focused on the first two. Using the most similar/different comparative approach, we conducted field research in the three success cases and secondary research in all five. This in itself is revealing as comparing Chile to the East Asian cases has not been done before. We find similarities among the success and failures of the first two categories. Therefore, we must dismiss them as having limited effectiveness. Given such conditions are necessary but insufficient, we turn to a third category, namely the culture around civil services, finding there are clear contrasts in the culture of success and failure cases. Moreover, all three of the former countries were at one time rife with corruption, thus raising the key question of how they changed. If there are cultural inflection points that can be identified that line up with the transformation of dishonesty to honesty in our three success cases, Singapore, Hong Kong, and Chile, and cultural sticking points in the other cases, we can explain the failure of formal reform efforts. This suggests we need a new research agenda on cultural change as a part of future reform efforts.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.062 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".