Notice bibliographique
Résumé
The first of this year's Topics in Cognitive Science issues is entirely devoted to excellent and award-winning research. We kick off with the newest contribution to the (ongoing) topic aimed at introducing the Fellows of the Cognitive Science Society (outlined in Bender, 2022), so far featuring articles by Cleotilde “Coty” Gonzalez (Gonzalez, 2022), Steven Sloman (Sloman, 2022), Jenny R. Saffran (Ruba, Pollak, & Saffran, 2022), and Michael J. Spivey (Spivey, 2023). This exclusive group is now joined by Barbara Malt from Lehigh University (US), who was elected Fellow of the Cognitive Science Society in 2022. In her paper “Representing the world in language and thought,” Malt (2024) provides an overview of her lifelong research program. Leveraging a remarkable variety of methodological paradigms, settings, and samples, this research has investigated the relationship between the content of thoughts and the words expressing them, and explored to what extent the latter open a window into the former. As Barbara pointed out in a personal note to me, the invitation to write a Fellows paper came at the right time, just as she was preparing for retirement. My wish is for her to fully embrace this new freedom, allowing herself to devote more time to the activities she holds dear, while I also hope that, even in retirement, she will continue to be a source of inspiration for the field of cognitive science. Incidentally, the final topic in this issue, too, presents Best Papers, in this case from the 20th International Conference on Cognitive Modeling in 2022. For this topic, Terrence C. Stewart (National Research Council Canada) has assembled the revised and expanded versions of the four top-ranked papers across both the virtual and in-person events of that year's ICCM, which was the first fully hybrid version of the conference. As Stewart points out in his introduction, these papers leverage cognitive modeling to test novel computational theories, account for the generation of high-level phenomena from low-level components, and develop novel explanations of human performance in complex tasks. 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.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,014 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,307 | 0,218 |
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 ».