Remembering Unethical Acts from the Past: A Path Toward Moral Repair
Bibliographic record
Abstract
Organizational wrongdoing is of central concern to organizational scholars. These discussions have focused on how organizations react to and manage these unethical acts. What has yet to be understood is how organizations engage with unethical acts from their past. In particular, we discuss how organizations engage with the discovery of unethical acts from the organization’s past and/or those acts from the past that are now considered unethical. These acts, although perpetrated in the past, are still painful and traumatic. We propose that for organizations to best engage with their past actions, they have to understand that there are different types of unethical acts from the past and that different forms of memory work needed to remember these unethical acts. Our paper presents a a typology of unethical acts from the past and we explain why forms of memory work should be used to help organizations engage in moral repair.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".