Bibliographic record
Abstract
W.H. Vivian Lee is principal and founder of LAMAS. Her work focuses on the role of craft in architecture as related to labor, professional practice, vernacular traditions, and ornament. She has extensive experience in the design and construction of public space including the East River Waterfront in Lower Manhattan. In addition to her role at LAMAS, Vivian is also Assistant Professor of Architecture at the University of Toronto and previously at University of Michigan. Prior to founding LAMAS, Vivian practiced as a project manager at SHoP Architects and LTL Architects in New York City. Lee received her masters of architecture from Harvard’s Graduate School of Design. She holds a B.A. in studio arts from Wesleyan University. James Macgillivray is a principal and founder of LAMAS. He has published widely on film, architecture and projection. He is from Toronto and received his Masters in Architecture from Harvard’s Graduate School of Design and his B.A. in architecture from Princeton University. Prior to founding LAMAS he worked as a designer at Steven Holl Architects and as a project manager at Peter Gluck and Partners Architects. Alongside his work at LAMAS, James is also Lecturer at the University of Toronto.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.510 | 0.285 |
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".