Bibliographic record
Abstract
Abstract In the introduction of this book, we outlined a framework of solutions to counter denialism, built around: (1) resonant reframing, (2) source cues, (3) identification, and (4) visualization. Beyond rhetorical content, the delivery of these messages—where and how they are conveyed—plays a crucial role. Thus, we consider which 5) comprehensive discursive, relational, and material interventions could be most effective in addressing different forms of science denialism. To illustrate these approaches, we revisit the case of Xylella fastidiosa, a bacterium that has devastated olive groves in Southern Italy since the early 2010s, causing severe economic harm and deepening social divisions, especially within the local community. By reconstructing the arguments of those who questioned scientific findings and recommendations—including politicians, intellectuals, local leaders, farmers, and judicial authorities—we reveal how the three levels of denialism interacted with the discursive, relational, and material dimensions of the crisis. This not only obscured the public’s understanding of Xylella fastidiosa but also delayed the implementation of timely solutions. We also assess how science-based interventions were proposed by experts, analysing why some succeeded while others faltered. Ultimately, this chapter offers a clear, theoretically grounded template for countering science denialism and mitigating its effects.
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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) | 0.028 | 0.002 |
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".