Urban flashes Asia : new architecture and urbanism in Asia
Bibliographic record
Abstract
Editorial (Helen Castle). Introduction: Dirty Cities (Nicholas Boyarsky). Chinatown is Everywhere (Peter Lang). Introduction: Micro--Urbanism (Ti--Nan Chi). Way of Display: Urban Tactics in the Context of the Betel Nut Culture in Taiwan (Karl--Heinz Klopf). Anarchy and Beyond: An interview with Kazuo Shinohara (Hirohisa Hemmi). What is Made in Tokyo? (Yoshharu Tsukamoto). Hyper Complex Living (Nobuyaki Furuya). Gaikoku Mura: Japanese Foreign Country Villages (Sue Barr). In the Age of Indeterminacy: Towards a Non--Visual Pragmaticism (Gary Chang). Pearl River Delta: Lean Planning, Thin Patterns (Laurent Guiterrez & Valerie Portefaix). Bangkok: Liquid Perception (Brian McGrath). Reconstruction Solidarity: The Thao Tribe (Nicholas Boyarsky). Action (Verb) Taipei (Sand Helsel). Modern Heritage: A Terrain of the Question (Guyon Chung). Hanoi (Justine Grahame). Contributors Biographies. AD+ Building Profile Great a Bambooa Wall (Jeremy Melvin). Practice Profile: Yung Ho Chang (Jayne Merkel) Engineering Exegesis Bridging the Gap with Collaborative Design Programs for CAD/CAM (Andre Chaszar). Interior Eye: Architecture Without Architects (Craig Kellogg). Urban Entropy: A Tale of Three Cities (Thomas Deckker). Site Lines: Gooderham and Worta s Distillery, Toronto, Canada (Sean Stanwick).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".