Truth and opinion in climate change discourse: The Gore–Hansen disagreement
Bibliographic record
Abstract
In this paper, I discuss the "inconvenient truth" strategy of Al Gore. I argue that Gore's notion of truth upholds a conception of science and policy that narrows our understanding of climate change discourse. In one notable exchange, Gore and NASA scientist, James Hansen, disagreed about whether scientific statements based on Hansen's computer simulations were truth or opinion. This exchange is featured in An Inconvenient Truth, yet the disagreement is edited from the film and presented simply as an instance of Hansen speaking "inconvenient truth". In this article, I compare the filmic representation of Hansen's testimony with the congressional record. I place their exchange in a broader historical perspective on climate change disputation in order to discuss the implications of Gore's perspective on truth.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.012 |
| 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".