Conflicting Loyalties: Cognitive Abstraction Drives Whistleblowing Behavior Among Those Who Value Loyalty
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
Abstract Potential whistleblowers, that is, people contemplating revealing potentially damaging information about unethical or unlawful behavior to a third party, are often described as facing a conflict between loyalty and fairness. Yet, whistleblowers often may feel a sense of conflicting loyalties : loyalty towards the party (e.g., a colleague) that may be damaged by their blowing the whistle and loyalty towards the party (e.g., society at large) that may benefit. Understanding how people deal with such conflict of loyalties is critical for increasing whistleblowing and reducing unethical behavior. In three studies (total N = 929), we draw on construal level theory to demonstrate that, when loyalty motives are salient, the level of abstractness at which people construe a whistleblower dilemma affects whistleblowing behavior. Because the party that stands to benefit from whistleblowing is typically more global than the party that will be damaged, cognitive abstraction increases whistleblowing behavior relative to concreteness, particularly when loyalty (vs. fairness) is a salient motive. Moreover, Study 3 findings reveal that cognitive abstraction predicts whistleblowing through increased identification with global entities among people for whom loyalty is more salient. Hence, we demonstrate that whistleblowing decisions are influenced not only by the salience of certain moral motives, but also the way that people construe whistleblower dilemmas, namely, relatively abstractly or concretely. Altogether, our research offers a novel understanding of whistleblowing behavior—as a conflict between loyalties—and identifies a cognitive mechanism for promoting whistleblowing and reducing unethical behavior.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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