Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
In the recent debate about the unity and integration of cognitive science (Núñez et al., 2019, 2020; see also commentaries in topiCS 11:4, introduced by Gray, 2019), one focus of the argument rested on the predominance of cognitive psychology and the displacement of smaller disciplines, such as anthropology and philosophy. Much less attention has been paid to the fact that another key player in the genesis of cognitive science has been withdrawing from the joint endeavor: artificial intelligence (AI) and computer science (Forbus, 2010; Goel, 2019). Although not framed in terms of the recent debate, the two topics in the current issue of topiCS are germane to this concern, as they both focus on cognitive modeling—arguably a signature approach of cognitive science and a natural link to AI. The call for rapprochement is clearest in the first topic, Cognition-Inspired Artificial Intelligence, edited by Daniel N. Cassenti (DEVCOM Army Research Laboratory), Vladislav D. Veksler (Caldwell University), and Frank E. Ritter (Pennsylvania State University). To showcase how cognitive science has not just benefitted from advances in AI, but can and should inspire AI development, Cassenti, Veksler, and Ritter bring together contributions from researchers actively using cognitive modeling to tackle a wide range of cognitive phenomena. Incidentally, the other topic in this issue seconds this call for greater attention to and consideration of cognitive models by presenting spearheading work in this very field. For their topic, Terrence C. Stewart (National Research Council Canada) and Joost de Jong (Maastricht University) have assembled revised and expanded versions of the five best papers presented at last year's 19th International Conference on Cognitive Modeling, a conference devoted to computational systems that are aimed at reflecting the internal processes of the mind. In their introduction, Stewart and de Jong point out how these papers, despite their diversity in content, still complement one another in terms of focus and approach: by refining and advancing computational models to better reflect empirical data, or by using such models to better explain data. Congratulations to their authors for their awards––we hope you continue the outstanding work you are doing! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstracts and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 découle