Bibliographic record
Abstract
Pour avoir mal ou pas du tout prévu la crise de 2007, les économistes se trouvent aujourd’hui en position d’accusés. Il est donc urgent et nécessaire d’examiner lucidement la façon de concevoir la prévision économique. Nous identifions ce que doivent être, selon nous, les principes épistémologiques d’une pratique prévisionnelle consciente de ses propres limites et ouverte sur le monde et sur les autres disciplines, à partir de l’instruction de trois questions : (i) quels sont la nature et le statut des énoncés « prévisionnels »?, (ii) quelle est la validité des énoncés sur le futur? et (iii) quelles sont les relations entre science et prévision? Nous insistons, dans cet examen, sur le caractère conjecturel des énoncés prévisionnels. Nous concluons en plaidant pour une conception sémiologique de la prévision économique et sur la pertinence du paradigme de l’indice.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".