Controls and Cooperation in Interactive and Non‐Interactive Settings
Bibliographic record
Abstract
ABSTRACT Prior research finds that controls that induce cooperation among collaborators on a project increase trust, and that this increased trust increases subsequent cooperation among collaborators. We extend this work by investigating how controls influence cooperative behavior in two settings. The first is an interactive setting where people work together and can benefit from each other's work. The second is a non‐interactive setting where people do not work together directly but where behavior can be observed. We propose that because controls are likely to engender greater trust and reciprocity in interactive settings than in non‐interactive settings, the effect of controls on future cooperative behavior will be greater for controls in interactive settings than for controls in non‐interactive settings. We find that controls in both settings increase future cooperative behavior, but the effect is significantly greater in interactive settings (where reciprocity and trust are more likely to develop). Furthermore, this increased cooperation is observed in an uncontrolled task, suggesting that the control fosters trust in others rather than trust in the control. These findings suggest that the benefits of controls are more substantial in work environments characterized by extensive teamwork and where employees benefit from each other's work.
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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 | 0.006 | 0.025 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".