Beyond reduction: climate change adaptation planning for universities and colleges
Bibliographic record
Abstract
Purpose The purpose of this paper is to outline a unique six‐step process for the inclusion of climate change adaption goals and strategies in a University Climate Change Plan. Design/methodology/approach A mixed‐method approach was used to gather data on campus climate change vulnerabilities and adaption strategies. A literature review highlighted common themes in adaption research. Meetings, surveys, and a specialized workshop with climate scenarios were created to elicit campus and community input. Findings The majority of the peer‐reviewed and grey literature surrounding climate change adaptation planning is aimed at larger levels of organization than a University campus (e.g. nations, populations, regions, and cities). An original planning process was created to identify vulnerabilities, risks and strategies. Key vulnerabilities fell into three main areas of concern: energy, transportation, and built environment. Adaptation goals, objectives and strategies were outlined for the Dalhousie University Climate Change Plan, based on risk levels associated with vulnerabilities. Research limitations/implications The adaption survey and workshop was created for this research. Small improvements were suggested for future use. The six weather scenarios presented at the workshop emphasized extreme events. Some participants felt that scenarios should be developed that feature smaller climate changes over a longer period of time. The prioritization activity used to establish risk needed to clarify the definition of risk being used. Future scenarios could include more consideration of socio‐economic factors. Originality/value Specific planning frameworks to create campus‐level climate adaptation strategies are sparse. A unique planning framework and workshop was developed to identify key climate change adaption strategies for universities.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".