Dénombrer pour maitriser les dommages des catastrophes naturelles
Bibliographic record
Abstract
Dans une visée opérationnelle, la puissance technique des moyens de communication offre l’opportunité inédite de documenter et d’enregistrer systématiquement les catastrophes naturelles. Le paradoxe réside dans l’exposition croissante au danger alors que nos sociétés dépensent une énergie considérable à produire des indices chiffrés facilitant la réduction des catastrophes. Illustrée par des études de cas, la relation entre chiffrage et liste de bénéficiaires conditionne la production et les usages sociopolitiques du décompte. Enregistrés dans des bases de données, ces chiffres semblent gagner en impartialité en neutralisant l’évènement. Néanmoins, l’examen des différences méthodologiques entre EM-DAT (The International Disaster Database) et DesInventar révèle des positionnements conceptuels divergents. Utilisé comme indicateur ou comme preuve, le chiffrage des impacts légitime un mode de gestion du territoire et des activités, soit libéral, soit prescriptif.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".