Composition, Rhetoric, and Literacies in Architecture Studio Classes
Bibliographic record
Abstract
This qualitative ethnographic study investigates the multimodal literacy practices and rhetorical strategies of undergraduate architecture students and studio professors in the first-year, third-year, and fifth-year studio classes of a five-year B. Arch. program. As they assume the disciplinary identity of architects, students develop studio ethos, a network of discipline-specific practices and values shaped by studio culture. Data was gathered through field observations, interviews, collection of textual artifacts, and photographs documenting students' visual work and presentations. Using a constant comparative analysis approach, I identify similarities between the design studio pedagogy practiced by the studio professors in this study, sophistic rhetorical pedagogy, and the pedagogy of multiliteracies developed by the New London Group of multimodal literacy theorists. Analysis of the data reveals a shifting relationship between verbal and visual literacies across the arc of the program. First, verbal literacy practices scaffold the development of discipline-specific visual literacies as novice students produce, translate, and synthesize knowledge by working iteratively across multiple modes. Then, the visual displaces the verbal as students present design arguments to an architectural audience. At the same time, verbal peer critique and presentations to non-architects require an increased rhetorical awareness. Finally, the verbal and visual are realigned according to disciplinary values in the fifth-year students' formal design thesis papers and independent thesis projects. A rhetorical analysis of the architects' practices reveals a conceptual connection to three components of sophistic rhetorical pedagogy: melete, the belief in the transformative power of iterative practice through agonistic encounters; kairos, the sense of appropriate and timely response; and metis, a flexible, cunning intelligence. I theorize that the relationship between multimodal literacy and rhetoric hinges on the interplay of modal affordances(what a particular mode can and cannot convey) and the available means of persuasion(rhetorical exigencies determined by cultural values). I argue that understanding the academic multimodal and rhetorical practices of a visually-based discipline can enhance how new media texts are composed and deployed in composition and rhetoric and literacy studies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".