Bibliographic record
Abstract
Contributors. An Introduction (Robert W. Kolb). Part I: Insights of Empirical Research. 1. Executive Preference for Compensation Structure and Normative Myopia: A Business and Society Research Project. (Diane L. Swanson and Marc Orlitzky). 2. Does Firm Performance Reduce Managerial Opportunism?: The Impact of Performance-based Compensation and Firm Performance on Illegal Accounting Restatements. (Jegoo Lee, Byung-Hee Lee, Sandra Waddock, and Samuel B. Graves). 3. A Preliminary Investigation into the Association between Canadian Corporate Social Responsibility and Executive Compensation. (Lois S. Mahoney ad Linda Thorne). Part II: Justice-based Analyses of Executive Compensation. 4. How Much is Too Much? A Theoretical Analysis of Executive Compensation from the Standpoint of Distributive Justice. (Jared Harris). 5. Justice, Incentives, and Executive Compensation. (William H. Shaw). 6. CEO Compensation and Virtue Ethics. (Michael Potts). 7. Chihuahuas in the Gardens of Corporate Capitalism. (Lyla D. Hamilton). Part III: Broadening the Perspective. 8. The Obligation of Corporate Boards to Set and Monitor Compensation. (Carmen M. Alston). 9. Executive Pay in Public Academia: A Non-Justice-Based Argument for the Reallocation of Compensation. (James Stacey Taylor). 10. How to (Try to) Justify CEO Pay. (Jeffrey Moriarty). 11. Executive Compensation: Just Procedures and Outcomes. (Joe DesJardins). 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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".