Nurturing Integrity in Management Education with the Development of an Alternative Web of Metaphors
Bibliographic record
Abstract
In recent years management education has sought to integrate into both undergraduate and graduate programs a concern for ethics and integrity. If this goal is to be achieved management educators must address the way in which an overreliance on mainstream metaphors (e.g., business-as-war) perpetuates an approach to management which is at odds with ethics and integrity. They need to be mindful of how metaphors are used and the images that they evoke. Part of the challenge in fostering ethics and integrity is to challenge the preconceptions which students have about the nature of business activities. Such attitudes are generally in line with these mainstream metaphors. In this chapter, the authors’ goal is not to find the perfect metaphor; one which will best incorporate a praxis of integrity as a part of management education. Rather they suggest that overuse of any metaphor has distorting effects and that what is the needed is to develop a web of metaphors which will provide management students with a capacity for seeing events from a broader perspective which includes considerations of ethical and value implications. Exposure to different metaphors will lead to different lines of reasoning and decision-making. By using different metaphors to understand the complex and paradoxical character of management, students have the opportunity to see possibilities for action and implications of decisions that they may not have thought about otherwise. In short, it is their claim that management education needs metaphorical pluralism if it is to nurture ethics and integrity.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".