Training managers for sustainable development: the lens of three practitioners
Bibliographic record
Abstract
How to create a context in and around the classroom to motivate business students to become global citizens and managers aware of the issues around sustainable development that are usually ignored by traditional academic teaching? How to make them aware of the issues, encourage them to take action, and enable them to act responsively? This chapter aims to build on our experience as teachers and educators in Business, Economics and Management education in our respective contexts-teaching sustainable development and management in a Business School in Canada; global environmental and social issues in an MBA program in finance and in a Business School in France; and Environmental Impact Assessment in a Department of Economics at a French university. It comprises three sections. The first describes our three experiences teaching sustainable development in the classroom to managers. The second section highlights how each of these experiences enhances the skills necessary for education in sustainable development based on Tilbury and Wortman's (2004) work. The last section summarizes some of the lessons learned and proposes future directions for research and practice.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".