Inoculating Against Moral Disengagement Creates Ethical Adherence for Narcissism
Bibliographic record
Abstract
Increasing honesty is critical in modern society. Moral Disengagement Tactics (MDTs) enable individuals to engage in unethical behavior while avoiding self-criticism, making MDTs a form of self-persuasion. One way to prevent persuasion is inoculation. Across three experiments ( N = 972), two preregistered, we randomly assigned individuals to a code of ethics versus inoculation to MDTs condition. Study 1 ( n = 443) found that those high in narcissism reported increased ethical intentions in the inoculation condition. Study 2 ( n = 224) replicated and extended this effect, finding that individuals high in narcissism were more likely to behave honestly in the inoculation condition. Study 3 ( n = 305) was a longitudinal study finding that inoculating those high in narcissism led to fewer lies over the past week’s inoculation. None of these interaction patterns emerged for Machiavellianism or psychopathy. Thus, inoculation to MDTs appears effective in reducing dishonesty among those high in narcissism.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".