Bibliographic record
Abstract
Research suggests that when individuals have done a good deed, this grants them `license' to engage in more self-interested, immoral or asocial behaviors that otherwise would have discredited the individual. A number of studies across disciplines have found evidence of such licensing effects, yet our understanding of what causes these effects is limited. It is clearly counterproductive if a good deed is likely to be followed by a bad one. Such a regulatory pattern threatens people's moral integrity and undermines personal welfare, yet no research examines how to counteract it. In Essay 1 "Removing Individuals' License to Misbehave," I present an intervention aimed at counteracting the licensing effect. I demonstrate that having participants engage in a physical act of closure - enclosing a written recall of their good deeds within an envelope - counteracts their licensing behavior. This intervention targets what I propose is a critical but overlooked condition for moral licensing to occur: the accessibility of one's previous good deeds. Furthermore, I contribute to our understanding of moral licensing by examining a novel moderator of the licensing effect, the actor-perceived specialness of one's good deed. Research has found that changes in self-concept may mediate the licensing effect. In Essay 2 "Examining Self-Concept as a Mediator of Licensing Effects", I critically examine this process. Across three studies I replicate the licensing effect, but find no significant relationship with participants' self-concepts. In each study, self-concept was measured using scales previously established in the licensing context, yet none of these mediated participants' licensing behavior. Based on both theoretical as well as empirical findings, I propose that our current self-concept measures need to be re-examined.By advancing our understanding of moral licensing behaviors and demonstrating how to counteract them, this dissertation provides significant practical and theoretical contributions for our understanding of moral licensing effects.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".