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Record W7126284773

Fossil fuel industry influence in higher education: A review and a research agenda

2024· article· en· W7126284773 on OpenAlexaboutno aff

Bibliographic record

VenueMURAL - Maynooth University Research Archive Library (National University of Ireland, Maynooth) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
FundersNational Institute on AgingNational Science Foundation
KeywordsFossil fuelClimate changeDenialTransparency (behavior)ScholarshipHigher education
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.086
GPT teacher head0.382
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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