Co-learning partnerships and carbon management in Denmark and Canada
Bibliographic record
Abstract
Purpose Climate change poses risks to society and the demand for carbon literacy within small and medium-sized enterprises is increasing. Skills and knowledge are required for organizational greenhouse gas accounting and science-based decisions to help businesses reduce transitional risks. At the University of Copenhagen and the University of Northern British Columbia, two carbon management courses have been developed to respond to this growing need. Using an action-based co-learning model, students and business are paired to quantify and report emissions and develop climate plans and communication strategies. Design/methodology/approach This paper draws on surveys of businesses that have partnered with the co-learning model, designed to provide insight on carbon reductions and the impacts of co-learning. Data collected from 12 respondents in Denmark and 19 respondents in Canada allow for cross-institutional and international comparison in a Global North context. Findings Results show that while co-learning for carbon literacy is welcomed, companies identify limitations: time and resources; solution feasibility; governance and reporting structures; and communication methods. Findings reveal a need for extension, both forwards and backwards in time, indicating that the collaborations need to be lengthened and/or intensified. Balancing academic requirements detracts from usability for businesses, and while municipal and national policy and emission targets help generate a general societal understanding of the issue, there is no concrete guidance on how businesses can implement operational changes based on inventory results. Originality/value The research brings new knowledge to the field of transitional climate risks and does so with a focus on both small businesses and universities as important co-learning actors in low-carbon transitions. The comparison across geographies and institutions contributes an international solution perspective to climate change mitigation and adaptation strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".