Notice bibliographique
Résumé
James Burke (1996), in his fascinating book The Pinball Effect , demonstrates the curious and unexpected paths of influence leading to most scientific discoveries. People often speak of the ‘marriage of ideas’. If this is true then the most prolific intellectual offspring come not from the arranged marriages preferred by research administrators but from chance meetings, and even illicit unions. The popular view of scientific discoveries as being linear causal chains from idea to solution is profoundly wrong; a better image would be a tangled web with many dead ends and broken strands. If most present knowledge depends on unlikely chains of events and personalities, what paths of discovery have been deflected because the right people did not come together at the right time? Which historical developments in science have been changed because two people, each with one half of the solution, were prevented from communicating due to linguistic or disciplinary boundaries? The second stage in the development of modern structural equation modelling is a case study in such historical contingencies and interdisciplinary incomprehension. During the First World War, and in connection with the American war effort, Sewall Wright was on a committee allocating pork production to various states based on the availability of corn. He was confronted with a problem that had a familiar feel. Given a whole series of variables related to corn availability and pork production, how do all these variables interact to determine the relationship between supply and demand, and the fluctuations between these two? It occurred to him that his new method of path analysis might help. He calculated the correlation coefficients between each pair of variables for five years, giving 510 separate correlations. After much trial and error he developed a model involving only four variables (corn price, summer hog price, winter hog price and hog breeding) and only fourteen paths that still gave a ‘good match’ between observed and predicted correlations. He described his results in a manuscript that was submitted as a bulletin of the US Bureau of Animal Industry. It was promptly rejected, perhaps because officials at the Bureau of Agricultural Economics considered it as an intrusion onto their turf. Happily for Wright, he had also shown it to the son of the secretary of agriculture (Henry A. Wallace), who was interested in animal breeding and quantitative modelling.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».