Research Companion to Corruption in Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it