Communicated Values as Informal Controls: Promoting Quality While Undermining Productivity?
Bibliographic record
Abstract
Abstract We find that the effectiveness of piece‐rate compensation relative to fixed pay in a laboratory letter‐search task hinges on the presence or absence of a nonbinding statement to participants that the experimenter values correct responses. In the absence of the value statement, participants with piece‐rate rewards for correct responses generate more correct and incorrect responses than do their counterparts with fixed pay, correcting errors as they go along to maximize compensation. Essentially, piece‐rate compensation acts as an output control, incentivizing participants to maximize correct responses through a “produce‐and‐improve” strategy. The value statement suppresses this strategy because participants appear to perceive it as an input constraint, prompting greater initial care at the expense of lower overall productivity. As a result, the value statement eliminates the gains in correct responses that piece‐rate incentivized participants otherwise realize. Thus, in settings in which individuals can gain efficiency by working expeditiously and improving quality when necessary, our results suggest the possibility that organizations could be better off just letting incentive schemes operate, rather than emphasizing quality in ways that could overly constrain productivity.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".