Preface to the CJEP special section on numerical cognition.
Notice bibliographique
Résumé
This special section on numerical cognition is an outcome of the 33rd meeting of the Banff Annual Seminar in Cognitive Science (BASICS) held in May, 2014. Valerie A. Thompson, also of the University of Saskatchewan, and I were co-organisers of BASICS 2014. For the overarching theme of the conference we decided to focus on the field of numerical cognition, which, by no coincidence, happens to be my main research interest. The importance of numeracy to modern societies can hardly be overemphasised. We are immersed in numbers and basic calculations every day and rely heavily on technologies that would be impossible without sophisticated mathematical knowledge. In the world's increasingly computer-based technological economy, numeracy is at least as important to socioeconomic opportunities as literacy, although the latter has historically received far more research attention. Over the last four decades, however, there has been a major escalation of research in the behavioural and brain sciences directed to understanding numeracy skills. This research intersects numerous subfields including cognitive and neurological development, educational psychology, computational modelling, animal cognition, neuropsychology, as well as cognitive science and neuroscience, including brain imaging and brain stimulation research (Campbell, 2005; Cohen Kadosh & Dowker, 2015).When the opportunity to help organise BASICS 2014 came along, as a long-time contributor to numerical cognition research I set out to recruit top people in the field, hoping to include both researchers who helped establish numerical cognition as a core area of research and also rising stars who were already redefining the future of the field. To my absolute delight, the first five people on my list all agreed more or less instantly to attend and present at the 2014 conference. These included Daniel Ansari (Western University, Canada), Roi Cohen Kadosh (Oxford University, U.K.), Wim Fias (Ghent University, Belgium), Jo-Anne LeFevre (Carleton University, Canada), and Patrick Lemaire (Aix-Marseille Universite, France). One possible reason for the uniform enthusiasm among this distinguished group to present at BASICS 2014 might have been my status as one of the pioneers of this research area. Of course, on the other hand, the world-renowned scenic beauty of Banff National Park, fascinating and ever-present wildlife, extensive recreational opportunities, and exquisite cuisine promised might have also played a small role in our having successfully attracted this great group to the conference. But let us not forget that the historical record of BASICS presenters includes a virtual pantheon of cognition and neuroscience greats (Peter Dixon, a long-time co-organiser of BASICS from the University of Alberta, has maintained a record of the conference at http://www.psych .ualberta.ca/~pdixon/BASICS/2014/PastBASICSPage/index.html.BASICS provides each speaker (usually five presentations over 2 days) an hour and a half for their presentation and discussion period, so that a speaker has the opportunity to talk about their research in big-picture fashion. Given this, our selection of speakers was not based solely on their published contributions to the numerical cognition literature, but also on their reputations as fine speakers. By the end of the morning session on the second day of the conference (Jo-Anne LeFevre, Daniel Ansari, and Patrick Lemaire had presented) there was already a buzz around the conference that something special was in progress. By the end of the afternoon session (Wim Fias and Roi Cohen Kadosh presented) there was a strong vibe that the group of speakers had collectively provided a fascinating, exciting, even inspiring picture of the history and future of numerical cognition research. Immediately following Valerie Thompson's closing remarks after the last speaker, CJEP Editor-in-Chief Penny Pexman, who was in attendance, suggested to me that the set of BASICS 2014 presentations would potentially make an excellent special section for the journal. …
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 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,160 | 0,080 |
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 ».