Bibliographic record
Abstract
Abstract Despite our increasing knowledge of how the world works, scientific advances, and innovations, denialism seems to be accelerating. Writ large, science denialism represents a global existential crisis that erodes social cohesion and our collective capacity to confront crises like climate change, exceeding planetary boundaries, transforming our energy supply and demand systems, global pandemics, social inequality, and large-scale migration. The chapter begins by examining the existing research of denial that largely centres on individual-level causes, consequences, and solutions. We argue that denialism is also a collective defence mechanism; a ‘turning away’ from difficult or uncomfortable truths, entailing that we can only address denialism by examining the role of organizations and institutions in both creating and dispelling it. We begin by describing trust, mistrust, and distrust, and their intricate interplay with science denialism. Then, we discuss the social-psychological and organizational causes. From this, we develop our framework of proposed solutions: identification, source cues, resonant reframing, and visualization. We conclude with a discussion of how these solutions aim to address science denial and denialism.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.078 | 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; both teacher heads agree on what is shown here.
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".