Bibliographic record
Abstract
In this article, design competitions, as they are practiced in Canada, are understood as devices that allow the study of interdisciplinary and intercultural dimensions of architecture. From the construction of the brief to the selection of the winning project, competitions are exemplary platforms for communicating design values. For example, competitor project proposals, which comprise many qualities, including constructive, material, and even political, represent the priorities of each design team, in the form of a place. Jurors debate each of these qualities through their own expertise. In their search for excellence, the competition jury is then an exemplar contact zone. By examining the various documents produced in this process, we can uncover the value systems of the many stakeholders. Observations of jury deliberations and analyses of jury reports can help expose how the diversity of jurors influences the selection of the winning project. Furthermore, in a contemporary context where environmental design is at the forefront, this diversity is especially interesting to study. An environmental expert’s evaluation of quantitative eco-measurements is very different from an architect’s judgment of spatial qualities and experiences. The focus of this article is to understand how such a variety of jurors influences the competition outcome.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.032 | 0.032 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".