Appeals to “Normality” and “Common Sense” in the Face of Global Uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At the time of writing, in the summer of 2024, we are confronted with a ‘polycrisis’ (e.g., Tooze 2022). This term is used to describe a situation in which multiple crises do not simply add up to a somewhat bigger crisis, but rather create a significantly different, amplified crisis in which the sub-crises influence each other in interdependent ways. As numerous studies have demonstrated (e.g., Heitmeyer 2024; Roberts 2022; Nowotny 2016), crises engender feelings of uncertainty, insecurity, and subsequently fear (Bauman 2006). The aim of this paper is to pose the overarching question: How do governments and citizens cope with such uncertainties? Les crises provoquent la peur, la panique, l’incertitude et l’impuissance. L’incertitude et l’insécurité mettent à l’épreuve tous les acteurs concernés ; chacun attend des instructions, une planification, des explications et la sécurité. Cependant, nous affrontons des alarmismes, des simplifications, une série de stratégies de légitimation et d’erreurs. Plus précisément, les erreurs sont souvent placées avant les intérêts communautaires, nationaux ou même locaux. Ces évolutions sont illustrées par une analyse qualitative et quantitative détaillée du discours des débats en Autriche, à l’été 2023. Je soutiens que les appels fallacieux au bon sens et à la normalité dépendent de leur contexte, avec des contenus, des fonctions et des effets différents observables. De tels appels instrumentalisent une « politique des émotions » de différentes manières. Ainsi, une nouvelle logique politique est normalisée, remplaçant le discours rationnel, la délibération et la formulation de politiques dirigées par des experts.
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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 | 0.001 | 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) | 0.000 | 0.000 |
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 it