Mutual reinforcement of academic reputation and fossil fuel divestment
Bibliographic record
Abstract
Non-technical summary By the end of 2020, 190 universities and colleges worldwide had publicly committed to divest partially or fully from fossil fuel holdings, to help mitigate global heating. We find a statistical correlation between the status of universities in the world rankings and decisions to divest endowments from fossil fuel. Further analysis suggests causation in both directions. Not only do the best divest, but divestors get better. Technical summary Previous studies have explored connections between environmental responsibility and the financial performance of business firms. Here, we explore connections between a particular form of environmental responsibility, divestment from fossil fuel, and the reputational status of a different form of organization, universities. We find a strong and robust link between world university rankings and commitments to divest endowments from the fossil fuel industry, with higher-ranked universities divesting at higher rates compared to lower-ranked universities. Rates of divestment also differ significantly between countries, and according to the political orientations of provinces and states. We do not find evidence for links between divestment treated as a binary variable and a university's number of students, size of endowment, or type of endowment. We use time lags to test whether the rank-divestment correlation may arise due to effects of rank on divestment and/or vice versa. These tests indicate influence in both directions. In light of these results, we predict universities that have not yet divested will face mounting peer pressure to do so. Social media summary Higher-ranked universities divest more frequently, and divesting universities improve more in the rankings.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".