Multimodality and socio-materiality of lectures in global universities’ media: accounting for bodies and things
Bibliographic record
Abstract
Lectures prevail as a ubiquitous teaching and learning method across universities worldwide. Whereas lectures have been conceptualized from language-centred perspectives, lectures' materiality as linked to their socio-cultural and historical meanings have been scarcely explored. To address this gap, we tackle the materiality of communication in ten live recorded lectures – collectively viewed more than 1,000,000 times – uploaded by ‘top-ranked’ universities in India, Japan, Russia, Egypt, Palestine, Spain, the USA, the UK, Italy and Canada, on their websites or YouTube media channels. The materiality we refer to comprises the key things/artefacts and bodies in the lectures. A multimodal semiotic analysis of non-verbal and material elements of a lecture is applied on the videos to first ‘map’ its material ingredients, and then explore associated meanings that form socio-material assemblages. The findings point at a few salient thing and body characteristics, such as the monofocal lecture platform, the omnipresent blackboard, the underrepresentation of female lecturers, and the low diversity and use of digital technology. We discuss these via the ‘body and thing idioms’ (Goffman, Behavior in Public Places: Notes on the Social Organization of Gatherings. New York: The Free Press, 1963) that mediate lecture hierarchies, historicity, meaning making and engagement, calling for wider acknowledgement of multimodality and socio-materiality in university practices.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".