Conflicting Loyalties: Cognitive Abstraction Drives Whistleblowing Behavior Among Those Who Value Loyalty
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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How this classification was reachedexpand
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".