Multimodality and the digital turn in teaching business discourse. An Introduction to the Special Issue
Notice bibliographique
Résumé
Digital technologies and multimodal discourseThe study of digital discourse emerged through the use of diverse mediated discourses to communicate information.The discourse analytical tools that had been originally formulated to analyse language use were then extended to the analysis of digital discourse and to studies on digital business discourse (Bargiela-Chiappini, 2009; Darics, 2015Darics, , 2016)).However, common patterns of interaction in the digital world are changing and new patterns of interaction have emerged, particularly those concerning socio-semiotic resources for online configurations of forms of interaction such as video, blogging and social networking (Sindoni, 2013).The dynamic combination of multiple symbols and semiotic resources within a specific communication context has resulted in the emergence of multimodal discourse (Liu et al., 2024).Thus the analysis of discourse includes various semiotic resources and the study of a diverse array of mediated communication modalities including words, images, colour and sounds in the interactive and compositional meaning-making process (Kress & van Leeuwen, 2001).These new interactive modalities blur the distinction between oral and written discourse in digital texts.They challenge the current way of conducting linguistic analysis as simply analysing oral and written texts, and require multimodal frameworks of analysis (Sindoni, 2013).In many ways, multimodality should always be part of digital discourse studies, as it has been considered a core concept in sociocultural linguistics and discourse analysis for some time (Kress & van Leeuwen, 2001).Given the increasingly multimedia and multimodal nature of digital communication and the growing complexity of multimedia formats and media, the study of language symbols, both verbal and nonverbal, provides a broader socio-semiotic perspective to digital discourse studies (Thurlow et al., 2020).In this way, speech and writing are considered language modes and, as semiotic resources, on a par with image, colours, sound, etc. (Sindoni, 2013).Liu et al. (2024) stress that discourse is a core research object with language as a key component of multimodal discourse studies (MDS).The authors find that applying semiotic resources across social media, identity, literacy, politics, education and gender illustrates MDS's broad scope and focus on knowledge construction and cognition, thus demonstrating interdisciplinary trends.While the literature in the field of multimodal studies is wide and varied for a number of disciplines, Liu et al.'s (2024) bibliometric analysis of MDS revealed that the study of multimodal discourse emerged gradually over the last 25 years.In fact, 2012 was the year when publications in multimodal discourse studies started to noticeably increase.On the other hand, of the most frequently discussed topics, only 17 publications concerned business disciplines compared to the top category, linguistics, with 496 publications.Overall, social sciences and humanities benefitted the most from multimodal discourse studies.Thus, this Special Issue fills this gap by providing a collection of activities for teaching and learning multimodal business discourse that are specifically tailored to the business communication context.
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 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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».