Managing architectural competitions: Empirical evidence from practices in the UK and Italy
Bibliographic record
Abstract
Since project based organizations are a typical configuration in the architecture industry, two streams of research are relevant for architecture practices. (1) Team management, as architecture design and production originate from collaborative networks among multiple actors, but results from empirical studies have been inconsistent regarding which variables are predictive of team performance and project success. (2) Project management, as management in organizing practices has grown in recent years, even if existing research has difficulty with linking performance attributes to specific factors such as organizational form, company culture or strategy. Based on these premises, the paper focuses on architecture competitions which are a currently debated topic and one of the most important rituals to acquire work. The aim is to explore how competitions are part of the practice's business strategy and how teams work on competitions' proposals. We analyse and compare two case studies of middle-sized architectural practices (around 30-40 employees), one in Italy and one in the UK, competing for work through competitions. Preliminary findings suggest that architectural competitions can serve both exploration and exploitation strategies and are based on a collaborative design process.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".