Engagement in greenhouse gas emission reduction : survey of best practices programs in engaging students, faculty and staff in behaviour change strategies
Bibliographic record
Abstract
This report seeks to identify best practices in engaging faculty and staff in GHG emission reduction. As most of the energy used on campus is used to heat and cool buildings, this report explores programs that can facilitate further engagement of staff, faculty members, researchers and students in green initiatives aimed at behaviour change. To explore this question, the top 20 universities that have decreased their emissions, based on the Princeton Review’s Guide to 352 Green Colleges (The Princeton Review, 2015), featured case studies at ASHEE resource website (Campus Sustainability Case Studies), and best practices featured in Climate Action Planning (Us Environmental Protection Agency, 2010) report were surveyed. Through the survey it was found that most universities and campuses that have reduced their GHG emissions have student peer mentoring programs, structured green certification programs and various opportunities for staff and students to design and participate in peer mentoring projects. To analyze which of these programs suits UBC and what can be learned from them, a matrix was developed to analyze the programs offered at each university. Overall it was found that having a strong communication strategy and peer-mentoring programs influence the degree by which students are able to change their behaviour. The three sections below illustrate the programs that either build on what UBC has been doing or have specific features that suit UBC. The first section focuses on programs for students living in residences, while the second focuses on programs for faculty, staff and researchers and the third section focus on specific behaviour of saving hot water. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".