Denying Anthropogenic Climate Change: Or, How Our Rejection of Objective Reality Gave Intellectual Legitimacy to Fake News
Bibliographic record
Abstract
The political rise of right‐wing populism in the United States, and elsewhere, has prompted a reexamination of theoretical perspectives that oscillate on an unequivocal rejection of objective reality. Indeed, populist campaigns that have acquired wide currency in the last few years have been ontologically predicated on the idea that there exists different “truths.” The premise of different truths has debunked any notion of an objective reality by rendering even the most reified of “facts” to be the outcome of individual subjectivities and ideological subscriptions. Donald Trump’s appeal to “fake news,” for instance, captures the implications that emerge when certain material “facts” become delegitimated in the public arena. The obfuscation of material facts through its entanglements in political discourse raises a timely question concerning theoretical resistance: How can objective reality retain its conceptual and analytical ideations without succumbing to the dangers that objective science historically created for socially marginalized subjects? Contextualizing the denial of anthropogenic climate change as an illustrative case, I answer this question by developing theoretical insights from critical realism and the notion of feminist objectivity. These insights accept the socially constructed nature of an objective reality but refute the idea of value‐free knowledge—that is, it disavows the claim that all representations of knowledge are equally valid and equally valuable.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| 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".