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Enregistrement W4399859555 · doi:10.3389/fpsyg.2024.1444503

Editorial: Reviews in language sciences

2024· editorial· en· W4399859555 sur OpenAlexaff
Antonio Benítez‐Burraco, Antonio Bova, Thomas L. Spalding

Notice bibliographique

RevueFrontiers in Psychology · 2024
Typeeditorial
Langueen
DomaineArts and Humanities
Thématiquelinguistics and terminology studies
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésPsychologyLinguisticsCognitive scienceCognitive psychologyPhilosophy

Résumé

récupéré en direct d'OpenAlex

resulted in a deeper understanding of what language is and how it is put into use for fulfilling different functions. Accordingly, we have gained a clearer view of language within the infrastructure of human cognition, including how it is processed by the brain, how it is impaired in people with pathological conditions, how it is acquired by the child, or how it evolved in our species, to name a few. Likewise, we have also made substantial progress in understanding the principles that govern human interaction through language, as observed in daily conversations, but also in many other speech events. Additionally, we now have comprehensive descriptions of thousands of language varieties across the world, from languages to sociolects to registers to styles, as well as detailed characterizations of the physical and sociocultural factors that contribute to regulating such linguistic diversity. Overall, this has notably improved our comprehension of the causes of linguistic diversity, and ultimately, the position of language(s) within human behavior. Methodologically, we now use more sophisticated tools and procedures for analyzing language at all levels, which has resulted in richer data about language facts, and ultimately, in more robust theories about language. Finally, we have also made a significant effort for translating all these discoveries to society, which has crystallized in e.g. better speech therapies aimed to help people with language disorders or more accurate language policies intended to regulate language use in complex, multilingual societies.All this progress has transformed language sciences into a dynamic and exciting field of research, but at the same time has made research in language sciences very demanding.Researchers in language sciences face several crucial challenges. First, it is not just that, as noted, the amount of data and evidence about language facts has increased exponentially. At present, a proper study of language is not possible without considering the data and evidence provided also by allied disciplines, like archaeology, history, ethology, genetics, or neuroscience, to name a few. In other words, language sciences have become increasingly multidisciplinary in nature. At the same time, and in part as a consequence of such multidisciplinarity, research in language sciences has become more and more demanding from a methodological perspective, resulting in a true technification in many fields. Consider, for instance, the sophisticated facilities used to examine how the brain processes language, the molecular techniques employed to determine the causes of language disorders with a genetic origin, or the Bayesian methods used to create language phylogenies. This methodological challenge is expected to increase during the next decades. Third, given such an impressive amount of assorted data about language is available, the time may have come to improve our hypotheses about the nature of language, which have been traditionally based on linguistic data (and theory) only. Theorizing better about language will be an additional challenge for the next decades too. Finally, as also noted, researchers in language sciences are more and more concerned about the necessity of transferring to society the results of their research, since there are indeed many problems that can benefit from a better understanding of language facts, from clinical linguistics, to artificial intelligence, to cultural mediation.Overall, because the field is increasingly multidisciplinary, methodologically complex, theoretically diverse, and applicable, and because research in all areas is growing exponentially, it is difficult for researchers to be up to date. Accordingly, reliable, open-source summaries of issues of interest for the field are more needed and more welcome than ever. The aim of this Research Topic is to gather comprehensive and actualized reviews of topics of particular interest within language sciences. We have brought together 6 contributions from 14 scholars.Reflecting current deep interest in second language learning, we have four reviews of specific areas of research on second language learning: Wang on memorization strategies, and, in particular, on the recent move to memorization of longer texts, rather than simply individual words; Dou, Chan, and Win on approaches to teaching second languages, particularly on the recent switch to English for Specific Purposes; Klimova and Seraj on the current role and future promise of chatbots in teaching second languages; and Qiau on the factors that affect second language learners, including motivation, aptitude, personality, intelligence, and learner preferences.In addition, Maggu, Kager, To, Kwan, and Wong present a meta-analytic review of work on the role of complex input in children with speech sound disorders, reflecting the importance of language science in providing clinical insights and treatments.Finally, Wie and Knoeferle review literature on how language relates to events in the world, their causal connections, and their representations, reflecting the deeply related nature of language and cognition.Given our interest in providing good, open-source, scientifically valuable reviews of the most current areas of language research, broadly conceived, we wish to end by drawing attention to another Frontiers in Psychology Research Topic: Reviews in Psychology of Language, which we hope will continue to provide a valuable resource to researchers in the area looking for comprehensive reviews across all areas of research in the psychology of language.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,129
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,035
Tête enseignante GPT0,351
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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