Fossil fuel industry influence in higher education: A review and a research agenda
Bibliographic record
Abstract
The evolution of fossil fuel industry tactics for obstructing climate action, fromoutright denial of climate change to more subtle techniques of delay, is undergrowing scrutiny. One key site of ongoing climate obstructionism identified byresearchers, journalists, and advocates is higher education. Scholars haveexhaustively documented how industry-sponsored academic research tends tobias scholarship in favor of tobacco, pharmaceutical, food, sugar, lead, andother industries, but the contemporary influence of fossil fuel interests onhigher education has received relatively little academic attention. We reportthe first literature review of academic and civil society investigations into fossil fuel industry ties to higher education in the United States, United Kingdom,Canada, and Australia. We find that universities are an established yet under-researched vehicle of climate obstruction by the fossil fuel industry, and that universities' lack of transparency about their partnerships with this industry poses a challenge to empirical research. We propose a research agenda of topi-cal and methodological directions for future analyses of the prevalence and consequences of fossil fuel industry–university partnerships, and responses to them.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".