Cooperative behavior in the workplace: Empirical evidence from the agent-deed-consequences model of moral judgment
Bibliographic record
Abstract
Introduction: Moral judgment is of critical importance in the work context because of its implicit or explicit omnipresence in a wide range of work-place practices. The moral aspects of actual behaviors, intentions, and consequences represent areas of deep preoccupation, as exemplified in current corporate social responsibility programs, yet there remain ongoing debates on the best understanding of how such aspects of morality (behaviors, intentions, and consequences) interact. The ADC Model of moral judgment integrates the theoretical insights of three major moral theories (virtue ethics, deontology, and consequentialism) into a single model, which explains how moral judgment occurs in parallel evaluation processes of three different components: the character of a person (Agent-component); their actions (Deed-component); and the consequences brought about in the situation (Consequences-component). The model offers the possibility of overcoming difficulties encountered by single or dual-component theories. Methods: = 1,349) to test this model. Results: moral judgments. These effects also varied depending on the levels of the Agent- and Consequences-component. Discussion: Thereby, the results exemplify the usefulness of the ADC Model in the work context by showing how the distinct components of morality affect moral judgment.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".