Building consensus: Design media and multimodality in architecture education
Bibliographic record
Abstract
This article explores multimodal communication and social interaction in university-level architecture education. Drawing on ethnography of North American programs of ‘design-build’ architecture, we consider how the judgment of a ‘good’ (or ‘bad’) design is as much a result of how it is communicated as what is communicated. In settings like the design ‘review’, students endeavor to persuade an audience of the merits of their proposed design. This is ideally accomplished through the ‘convergence’ of multiple design media on the same ‘idea’ or design gestalt. ‘Convergence’ involves not just technical competency; it is also a social achievement: an effect of composing and coordinating multimodal semiotic media according to shared representational and communicative conventions. Failure to recognize convergence is often an effect of intersemiotic dissonance. This is also the risk of a design’s failure in the eyes of the faculty jury, who often direct their critiques toward communicative inconsistencies.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".