Brian MacKay-Lyons: Instinctive and Quick
Bibliographic record
Abstract
Brian MacKay-Lyons is a driven man on an intense mission. Brian received his Bachelor of Architecture from the Technical University of Nova Scotia in 1978 and his Master of Architecture and Urban Design at UCLA. He learned from architect Charles Moore that people respect an open-handed way of dealing with a design project. He uses the abstraction of the architectural parti to invite his clients to leave their tastes at the door, and take a conceptual cruise with him. He&s;s found that people really get his abstractions. In 1985, he founded Brian MacKay-Lyons Architecture Urban Design, and 20 years later partnered with Talbot Sweetapple to form MacKay-Lyons Sweetapple Architects Ltd. Houses designed in Atlantic Canada have made Brian a leading proponent of regionalist architecture worldwide. This recognition has led to a transition in the practice toward increased public and international commissions. MacKay-Lyons is the Director of the Ghost Architectural Laboratory and a full Professor of Architecture at Dalhousie University.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".