Managing Reputation, Sustainability, and Self-Interest: The Case of CEO Remuneration in the United States and the Importance of Being Earnest
Bibliographic record
Abstract
Executive compensation has long been a lightening-rod of interest in the popular press and frequently makes the headlines. It seems that everyone has an opinion on the subject, with most demanding an end to inflated compensation packages. Depending on whether you are a member of the C-suite or not will likely skew your opinion on the matter. Given that the CEO is the most visible manifestation of the company to the outside world it is common to fixate on the way in which they are being compensated. However, after all of the research that has been conducted, we are still not sure about what factors determine a business executives’ pay. The present study seeks to add to the extant literature on the subject of CEO compensation by introducing a couple of promising new variables: corporate reputation and sustainability. It is argued that since the CEO is the face of the organization that he/she will be compensated based on how well they manage the firm’s reputation overall and its “environmental footprint” in particular.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".