Approaching Sustainable Energy Transitions Through Bringing Disciplines Together: an examination of Carleton University’s graduate cross disciplinary course in sustainable energy
Bibliographic record
Abstract
Complex challenges in sustainable energyrequire innovation: new ways of approaching problemsand new ways of collaborating. Bridging disciplinesthrough teamwork is one way to address issues effectively. Drawing from the experiences of a graduate level crossdisciplinary course involving engineering and public policy at Carleton University, this paper seeks to provide insights on the practical side of bridging disciplines in the classroom. Offered since 2011, this is a core course of Carleton’s Master’s program in Sustainable Energy Engineering and Policy. Working in groups, students envision and develop plans for novel sustainable energy projects. Through our experiences we posit the following three suggestions for successful interdisciplinary teamwork. First, ensuring that students embarking on a course share a similar foundation. Second, lessons from literature regarding team dynamics can be applied to group project work. Thirdly, that group work, challenging at times, may later be found valuable in life beyond the classroom
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".