Worse than others but better than before: Integrating social and temporal comparison perspectives to explain executive turnover via pay standing and pay growth
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
Organizations often pay greater salaries to higher‐ranking executives compared to lower‐ranking executives. While this method can be useful for retaining those at the organization's apex, it may also incline executives at the bottom of the pay pyramid to see themselves at a disadvantage and thus exit the firm. Naturally, organizations often want to retain some of their lower‐paid, but highly valuable executives; the question, then, is how organizations can reduce the turnover of lower‐ranking executives. By integrating social with temporal comparison theory, we argue that, when executives earn relatively less than their peers, more pay growth (i.e., individual pay increases over time) leads to less turnover. The results of our analysis, which covered almost 20 years of objective data on a large sample of U.S. top executives, provide support for our theory.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it