For a Dollar, Would You...? How (We Think) Money Affects Compliance with Our Requests
Bibliographic record
Abstract
Research has shown a robust tendency for people to underestimate their ability to get others to comply with their requests. In five studies, we demonstrate that this underestimation-of-compliance effect is reduced when requesters offer money in exchange for compliance. In Studies 1 and 2, participants assigned to a no-incentive or monetary-incentive condition made actual requests of others. In both studies, requesters who offered no incentives underestimated the likelihood that those they approached would grant their requests; however, when requesters offered monetary incentives, this prediction error was mitigated. In Studies 3-5, we present evidence in support of a model to explain the underlying mechanism for this attenuation effect. Studies 3 and 4 demonstrate that offering monetary incentives activates a money-market frame. In Study 5, we find that this activation reduces the discomfort associated with asking, allowing requesters to more accurately assess the size of their request and, consequently, the likelihood of compliance.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".