Ethical Dilemma and Its Resolution: Managers’ Perspectives
Bibliographic record
Abstract
What situations can lead to an ethical dilemma in the workplace and how do managers deal with them? Based on different concepts of moral philosophy, this exploratory study analyses the nature of ethical dilemmas at work and managers’ attitude to cope with them. A qualitative analysis of interviews with managers from different regions of Québec (Canada), lead us to the following observations. Firstly, an ethical dilemma emerges from a classic tension between the organisational requirements and one’s personal values. The most common situation or source of dilemma involves the lay-off process. The ethical stakes associated with this kind of situation often refers to the protection of a manager's reputation and his efficiency at work. The second observation refers to the decision- making process involving ethics. When managers go throughout this process, in most cases they adopt an attitude described by Aristotle as Enkrates. From these observations, we suggest a theoretical model and a series of recommendations.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".