Introduction to <i>topiCS</i> Volume 13, Issue 3
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
With the current issue of Topics in Cognitive Science, we are proud to present award-winning research again, starting off with a scholar's lifetime achievements distinguished by the Rumelhart Prize, then introducing the Best Papers from the 18th International Conference on Cognitive Modeling. The first topic honors Michelene (“Micki”) T. H. Chi (Arizona State University), the 19th recipient of the David E. Rumelhart Prize. With a background in mathematics and a strong interest in the science of education, Chi has conducted pioneering, widely cited, and highly influential work on the active role of learners in the learning process, from self-explanations to the development of expertise. Having been active in the society for almost her entire academic life, and elected one of its inaugural fellows in 2003, she received the award also referred to as “the Nobel Prize in Cognitive Science” in 2019, for having “challenged basic assumptions about the mind” more than once and for having “defined new approaches that have shaped a generation of cognitive and learning scientists.” Following a pithy introduction by former topiCS editor Wayne Gray, we are happy to present the paper based on her Rumelhart Lecture, on “Translating a theory of active learning,” in which she attempts to close the gap between research and practice by outlining a multistep translation research framework. The second part of this issue comprises revised and expanded versions of the four best papers presented at the 18th International Conference on Cognitive Modeling, which took place last year fully virtually as was the case for so many other conferences. This batch has been curated by Topic Editors Terrence C. Stewart (National Research Council Canada) and Christopher Myers (Air Force Research Laboratory), who also introduce this selection of papers in more detail. Congratulations to all our 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 abstract 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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