Researching Wrongdoing and Irresponsibility Using Historical and Retrospective Approaches
Bibliographic record
Abstract
This presenter symposium provides a space for scholars from different areas of the Academy to come together to explore the potential gaps, linkages, and overlaps that exist at the intersection of research on organizational wrongdoing and irresponsibility and a methodological or conceptual engagement with the past. Specifically, we draw on a range of divisional experiences and approaches to explore (i) how organizations account for and manage their problematic past and (ii) the role memories and memory work play in historical and ongoing cases of wrongdoing. In doing so, the symposium will highlight the particular affordances and challenges that the past represents for understanding and tackling wrongdoing and irresponsibility. Three presentations will demonstrate specific historical and retrospective approaches, showing their potential value to organizational wrongdoing and irresponsibility research. Finally, we provide a space for dialogue on future directions and opportunities that stem from the intersection of these themes. Working Between Two Scaffolds: The Memory of Corporate Irresponsibility in an MNE Author: Andrew D A Smith; Birmingham Business School, U. of Birmingham, UK Author: Emily Buchnea; Newcastle Business School, Northumbria U. Author: Nicholas Wong; Newcastle Business School, Northumbria U. Author: Ian Jones; U. of York, UK Corporate Historical Integrity: The Financial Industry and Slavery (WITHDRAWN) Author: Sarah Federman; U. of San Diego Memory work following Corporate Social Irresponsibility: Learning from Mariana Dam Break in Brazil Author: Hamid Foroughi; Warwick Business School Author: Rajiv Maher; EGADE Business School, Tecnologico de Monterrey Forgetting and Remembering the Early Histories of Women in the Royal Canadian Mounted Police Author: Ellen Shaffner; Mount Saint Vincent U.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".