Appeals to “Normality” and “Common Sense” in the Face of Global Uncertainty
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.
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 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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.102 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".