An investigation into the relationship between the extent of climate change research and climate change action in universities
Bibliographic record
Abstract
Universities and affiliated research institutions produce a significant portion of climate change data. Researching at the bleeding edge of human understanding, they not only provide the data on climate change, but the means to face it. However, although some universities prioritize funding for climate change and preach urgent action and education, do they take measures themselves to reduce their own negative impact?Various Ontario Universities were analyzed based on publicly accessible data on public grants, total funding, student population, and greenhouse gas (GHG) emissions. The data was plotted using five-year moving averages to reduce local discrepancies. K-means analysis divided the data into four clusters of GHG per capita emitters. It was found that institutions that allocated relatively little funding varied in the per capita GHG emissions.However, it was discovered that universities who had more research funding allocated to climate change research had consistently lower emissions; all such universities fell into the two lowest emission clusters, and those with the highest funding into the lowest. This seems to suggest that some though not all universities are reducing their footprint regardless of how much they invest into climate change research, yet those who do put an emphasis on climate change research consistently have lower per capita GHG emissions. This finding adds credibility to the data coming from institutions that invest significantly into climate change research, and is a victory for climate change education.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".