Organizational evil and the responsibility of management and managerial practices
Bibliographic record
Abstract
How is it that ordinary people decide to take part in genocides? Philosophers and psychologists have attempted to provide explanations of how genocidal organizations (i.e. set-up to conduct executions) bear on the moral judgement of genocide participants. Here, I resituate those findings within the field of human resource (HR) management. I highlight how basic principles of management and HR management (selection of personnel, division of labour, reinforcement methods and others) can lead ordinary individuals to judge their participation in a genocide as acceptable. Although initially only designed to increase motivation and productivity, these techniques also affect individuals’ awareness of ethical issues. Consequently, participants shift their judgements in favour of the genocidal organization, forgetting the victims. Management studies have seldom addressed the topic of genocide, despite clues in the literature that genocides are organizational in nature. By combining the three fields of management, business ethics and genocide studies within an approach based on transdisciplinary analysis, I hope to show that genocides constitute a legitimate subject for management studies.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.083 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".