How the realized measure of a worker’s performance affects their perception of their compensation
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
Workers often struggle to fully appreciate the quality of their performance. Rather, workers use the measure of their performance that is realized from their firm’s measurement system, which is typically imperfect, as a guide to do so. This study examines how workers’ perceptions about their compensation depend on the realized measure of their performance. Our experimental results suggest that before performing a task, workers display a fairness sentiment whereby they expect compensation to decrease as the measure of performance suggests worse performance. However, once the measure of their performance is realized, workers’ fairness sentiments weaken, and they request higher-than-expected compensation, with this deviation increasing as the realized measure worsens. Thus, a realized measure of performance distorts workers’ perceptions about their compensation and their fairness sentiments. This suggests that the benefits of perceived “fair” worker compensation are less likely to occur once workers have realized measures of performance.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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