Bibliographic record
Abstract
Selon les recherches de Nassim Taleb sur les limites de la connaissance et le poids de l’imprévisibilité dans nos schémas épistémiques, un « Cygne Noir » est une figure métaphorique renvoyant à un événement imprévisible qui surgit et vient ébranler nos cadres normatifs. C’est une aberration qui fait rupture dans la cohérence de nos modèles encyclopédiques et qui force la reconsidération à la fois de nos connaissances établies et des modes d’acquisition et de validation de ces connaissances, voire de paradigmes entiers – soudainement reconnus comme erronés. La nécessité de rendre cohérente cette donnée aberrante produit des explications qui retracent des effets de prévisibilité rétrospectifs. Symétriquement, un événement hautement prévisible qui ne survient pas peut également être appelé un Cygne Noir. Le Cygne Noir symbolise et traduit ainsi la stupeur d’une conscience prenant acte des limites de l’induction et de la probabilité.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.159 | 0.061 |
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".