Sense-Remaking: Unpacking Ethical Judgment Change in a Business Ethics Course
Bibliographic record
Abstract
While business ethics (BE) courses have increasingly formed part of business school curricula, we still do not know much about how these courses can change students’ capacity to deal with ethical issues. Drawing on a sensemaking perspective, we conducted an action research study with 66 business professionals enrolled in an executive training program at a French university. The aim was to investigate the processes underlying ethical judgment (EJ) change through a BE course. Participants were invited to pick a significant ethical issue they had personally experienced at work. They were then asked to make sense of it, in writing, at the beginning and at the end of the course, 3 months later. In comparing pre-course and post-course judgments, we concluded that the structure and contents of the respondents’ initial judgment had indeed been modified. This change could be accounted for as the outcome of four ‘sense-remaking’ mechanisms, which we theorize as complexifying, reprioritizing, conceptualizing and contextualizing. Our study contributes to the literature on BE education by demonstrating the benefits of a sensemaking approach. It also offers an original process-based model of EJ, specifying the mechanisms at play in EJ change. Finally, it contributes to the field of sensemaking studies by introducing the concept of sense-remaking, shedding new light on the evolutive dimension of sensemaking.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".