Bibliographic record
Abstract
Contents: Introduction: Corruption in Organizations: Causes, Consequences and Choices Ronald J. Burke PART I: CAUSES OF CORRUPTION 1. Greed Ronald J. Burke 2. Individual and Organizational Antecedents of Misconduct in Organizations: What do we (believe that we) know, and on what bases do we (believe that we) know it? Joel Lefkowitz 3. Research on Corruption and Unethical Behavior in Organizations: The Use of Conjoint Analysis Robert Folger, Robert Pritchard, Rebecca L. Greenbaum and Deborah DiazGranados 4. The Escalation of Corruption in Organizations Stelios C. Zyglidopoulos and Peter Fleming PART II: CONSEQUENCES OF CORRUPTION 5. Labour Relations and Ethical Dilemmas of Extractive MNEs in Nigeria, South Africa and Zambia Gabriel Eweje 6. On the Corruption of Scientists: The Influence of Field, Environment, and Personality Michael D. Mumford, Alison L. Antes, Cheryl Beeler and Jay J. Caughron PART III: INDIVIDUAL AND ORGANIZATIONAL CHOICES 7. A Comparative Perspective on Corruption: Kantian, Utilitarian or Virtue? Rosa Chun 8. Ethical Leadership R. Edward Freeman, Brian Moriarty and Lisa A. Stewart 9. Corruption, Outrage and Whistleblowing Brian Martin 10. Organizational Responses to Allegations of Corporate Corruption Vikas Anand, Alan Ellstrand, Aparna Rajagopalan and Mahendra Joshi 11. Reducing Employee Theft: Weighing the Evidence on Intervention Effectiveness Edward C. Tomlinson 12. Corporate Ethical Codes as a Vehicle of Reducing Corruption in Organizations Betsy Stevens 13. Transparency International: Global Franchising and the War of Information Against Corruption Luis de Sousa and Peter Larmour 14. Canadian Corporate Corruption L.S. Rosen Index
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.114 | 0.035 |
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".