An Exploration of the Dishonest Side of Self–Monitoring: Links to Moral Disengagement and Unethical Business Decision Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The majority of research on self–monitoring has focused on the positive aspects of this personality trait. The goal of the present research was to shed some light on the potential negative side of self–monitoring and resulting consequences in two independent studies. Study 1 demonstrated that, in addition to being higher on Extraversion, high self–monitors are also more likely to be low on Honesty–Humility, which is characterized by a tendency to be dishonest and driven by self–gain. Study 2 was designed to investigate the consequences of this dishonest side of self–monitoring using two previously unexamined outcomes: moral disengagement and unethical business decision making. Results showed that high self–monitors are more likely to engage in unethical business decision making and that this relationship is mediated by the propensity to engage in moral disengagement. In addition, these negative effects of self–monitoring were found to be due to its low Honesty–Humility aspect, rather than its high Extraversion side. Further investigation showed similar effects for the Other–Directedness and Acting (but not Extraversion) self–monitoring subscales. These findings provide valuable insight into previously unexamined negative consequences of self–monitoring and suggest important directions for future research on self–monitoring. Copyright © 2013 European Association of Personality Psychology
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it