Dealing with Organizational Legacies of Irresponsibility
Bibliographic record
Abstract
Organizations are increasingly confronted with critical questions and concerns posed to their legacies of irresponsibility, in other words, the decisions and actions taken by past generations of managers deemed unethical and immoral and with enduring negative social and environmental consequences in the present. However, there is limited guidance for organizations and their managers on how to deal with such legacies. The purpose of this paper is to offer directions on how organizations can approach their troubled pasts. We draw from the literature on transitional justice to develop an approach that organizations can use to deal with their legacies of irresponsibility. This paper contributes to the literature on historic corporate social (ir)responsibility and provides practical guidance for organizations to address the sins of previous generations of managers.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.100 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.017 |
| 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, 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".