Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Although information searching is one of the most popular online activities people engage in for a variety of goals and tasks every day, search systems have long been viewed from a rather limited perspective. That is, search systems have been typically viewed as tools for retrieving online content to satisfy information needs. However, today’s search systems support people’s interactions with information and help people access and use information in ways that go beyond offering a set of search results for specified search tasks. Despite the fact that information search systems have evolved from information-retrieval tools to full-text information-intensive systems over the past two decades, researchers have only recently started recognizing search systems as rich online spaces in which people can learn and discover new knowledge while interacting with online content. This does not mean that searching and learning have not been seen as connected in the field of information science. In fact, there have been numerous studies on the intersection between searching and learning. However, the association between searching and learning has often been defined in terms of searching in the learning environment, having learning as a search goal or learning about searching, focusing on teaching search and evaluation skills to youth. As a result, the concept of learning has often been assumed rather than clearly being articulated in most information science studies. A new research direction we present in this special issue is ‘Searching as Learning’, which attempts to move away from rather simplistic conceptualizations either as searching to learn or learning to search. From the perspective of searching as learning, we propose to reconsider the value of search systems in supporting human learning directly while focusing on the impact, influence and outcomes of using search systems with respect to a learning process. We believe that there are great opportunities to leverage and extend current search systems to foster learning by reconfiguring search systems from information-retrieval tools to rich learning spaces in which search experiences and learning experiences are intertwined and even synergized. The idea of studying and designing search systems to foster learning during the search process and create a rich learning space has been attracting growing recognition among researchers and practitioners in recent years. This Special Issue is a follow-up to the Searching as Learning (SAL 2014) workshop ( held in conjunction with the Information Interaction in Context (IIiX) Confe
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 | 0,010 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,027 |
| Science ouverte | 0,006 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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