Bibliographic record
Abstract
Abstract. Climate change awareness is floundering across the globe despite climate change education being embedded in international treaties to address the climate crisis – the United Nations Framework Convention on Climate Change (the UNFCCC) and the subsequent Paris Agreement. The Intergovernmental Panel on Climate Change (IPCC) acknowledges forces hostile to climate awareness and education – namely, climate denial sponsored by the energy-industrial complex. Climate change is studied by the physical sciences, but climate denial is the purview of the social sciences; the latter has revealed the why and how of climate denial. Climate-denial organizations (which directly deny aspects of the scientific consensus on climate change) and the related petro-pedagogy groups (which teach that oil is a benefactor to humanity, but say little about the connection of fossil fuels to the climate crisis) have arisen to attempt to interfere with the teaching of the science of climate change in school classrooms. These organizations were found in the United States, Canada, and some European nations (this review is mainly restricted to English-language sources). This review aims to (1) provide an overview of climate denial, promoted and funded by the energy-industrial complex; (2) identify and examine organizations involved in climate denial in schools; (3) summarize the strategies of climate-denial organizations in school classrooms; and (4) put forward recommendations for further research and action.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".