Multimodal Academic Discourse Socialization: Examining Geoscience Students’ Disciplinary Knowledge Construction and Socialization at a Canadian University
Bibliographic record
Abstract
Abstract This ethnographic multiple-case study examines how undergraduate students are socialized into the disciplinary norms, values, and practices of a geoscience course at a Canadian university. Transcending logocentric assumptions about academic discourse, this article advances a broader domain of inquiry––multimodal academic discourse socialization––which foregrounds the polysemiotic nature of academic socialization. This approach examines not only linguistic but also a wider range of semiotic resources, including gestural, visual, material, and spatial ones, among others. To understand geoscientists’ disciplinary norms, values, and communicative practices, ethnographic data (classroom observations, semi-structured interviews, course-related artefacts) were thematically analysed. Focal students’ geoscience poster presentation performances were also analysed using multimodal interaction analysis to scrutinize micro-level instantiations of disciplinary practices. Findings highlight how students were socialized into geoscience ‘observations and interpretations’ through a recurrent multimodal classroom activity, which was also reflected in micro-level multimodal practices enacted in students’ geoscience poster presentations. This study emphasizes that multimodal enactments constitute a crucial dimension of disciplinary practices and values connected with learning to think, view, and represent knowledge as geoscientists.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.020 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".