Integrating Programmatic Expertise from across the US and Canada to Model and Guide Leadership Training for Graduate Students in Sustainability
Bibliographic record
Abstract
It is critical that future sustainability leaders possess the skills and aptitudes needed to tackle increasingly ‘wicked’ challenges. While much has been done to identify this need, inadequate Leadership Training for graduate students in Sustainability (LTS) continues to plague even the most highly-resourced institutions. Collectively, the authors of this paper represent the small yet growing number of LTS programs across the United States and Canada working to close this training gap. In this paper, we describe the integrative approach we took to synthesize our collective knowledge of LTS with our diverse programmatic experiences and, ultimately, translate that work into concrete guidance for LTS implementation and design. We present a framework for the suite of key LTS aptitudes and skills yielded by our collaborative approach, and ground these recommendations in clear, real-world examples. We apply our framework to the creation of an open-access curricular database rich with training details, and link this database to an interactive network map focused on sharing programmatic designs. Together, our process and products transform many disparate components into a more comprehensive and accessible understanding of what we as LTS professionals do, with a view to helping others who are looking to do the same for the next generation of sustainability leaders.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".