Bibliographic record
Abstract
= 3,559) show that individuals experiencing regulatory fit versus nonfit are more likely to behave in manners consistent with their moral predispositions as assessed by the trait Moral Disengagement scale, the Machiavellianism scale, and the Honesty-Humility Subscale of the HEXACO-60 inventory. Following an experience of regulatory fit (vs. nonfit), participants with moral predispositions were more likely to consider the decision to engage in sexual intercourse outside a committed relationship as a moral issue (Study 1), and were less willing to do so (Study 2); they also expressed higher intentions of reporting income honestly for tax purposes (Study 3), imposed harsher punishment on a transgressor (Study 4), and self-sacrificed more for the common good in a social dilemma (Studies 4 and 5). The opposite was observed when participants with immoral predispositions experienced regulatory fit (vs. nonfit). In an incentivized sender-receiver game, participants with moral predispositions were less likely to lie for monetary gains when they experienced regulatory fit (vs. nonfit), whereas those with immoral predispositions were more likely to lie (Study 7). By operationalizing the regulatory fit experience as incidental to the moral decision context and assessing moral predispositions with at least a week lead or lag from the main experiment, the findings provide unambiguous evidence that regulatory fit impacts moral conduct by intensifying moral predispositions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".