Bibliographic record
Abstract
Winning the anticorruption battle: a guide for citizens and politicians. The phenomenon of corruption has existed since antiquity; from ancient Mesopotamia to our modern-day high-level ethical morass, people have sought a leg up, a shortcut, or an end run to power and influence. In this volume in the MIT Press Essential Knowledge series, Robert Rotberg, a recognized authority on governance and international relations, offers a definitive guide to corruption and anticorruption, charting the evolution of corruption and offering recommendations on how to reduce its power and spread. The most important component of anticorruption efforts, he argues, is leadership that is committed to changing dominant political cultures. Rotberg explains that corruption is the conversion of a public good into personal gain—either by the exchange of cash for influence or by the granting of special favors even without explicit payments. He describes successful anticorruption efforts in countries ranging from Denmark and Sweden to Canada and Costa Rica, and discusses the roles of judicial systems, investigative journalism, multinational corporations, and technological advances. He shows how the United States has become more corrupt than before, and contrasts recent US and Canadian experiences. Without sufficient political will to eliminate corruption, it persists. Rotberg outlines thirteen practical steps for battling corruption, including removing holdover officials tainted by corruption and the public declaration of financial assets by elected officials and appointees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".