“Everything Is in the Lab Book”: Multimodal Writing, Activity, and Genre Analysis of Symbolic Mediation in Medical Physics
Bibliographic record
Abstract
Writing and genre scholarship has become increasingly attuned to how various nontextual features of written genres contribute to the kinds of social actions that the genres perform and to the activities that they mediate. Even though scholars have proposed different ways to account for nontextual features of genres, such attempts often remain undertheorized. By bringing together Writing, Activity, and Genre Research, and Multimodal Interaction Analysis, the authors propose a conceptual framework for multimodal activity-based analysis of genres, or Multimodal Writing, Activity, and Genre (MWAG) analysis. Furthermore, by drawing on previous studies of the laboratory notebook (lab book) genre, the article discusses the rhetorical action the genre performs and its role in mediating knowledge construction activities in science. The authors provide an illustrative example of the MWAG analysis of an emergent scientist’s lab book and discuss its contributions to his increasing participation in medical physics. The study contributes to the development of a theoretically informed analytical framework for integrative multimodal and rhetorical genre analysis, while illustrating how the proposed framework can lead to the insights into the sociorhetorical roles multimodal genres play in mediating such activities as knowledge construction and disciplinary enculturation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".