Multimodality and the digital turn in teaching business discourse. An Introduction to the Special Issue
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".