Montreal 2012, Montreal: Canada (2012)" Shanghai, air and other stories Casual dissemination for a biological transformation
Bibliographic record
Abstract
Abstract. Two years ago I lived for 11 months in the 17 th floor of a high-‐rise building in Shanghai. My bedroom had a small bow window, a privileged observatory; wherefrom everyday I looked at the city. From there, there wasn’t architecture anymore, there wasn't social life; there was just a big pattern. On the contrary when I was walking on the street, Shanghai appeared to me a crowded city full of signs, people, messages and objects: a tremendous chaos. Looking from my win-‐ dow every thing was appearing differently: it was calm and diffuse. My glance was wondering in front of that view that every morning was changing. Every morning the city was more or less suspended, it was wrapped in a thinner or thicker layer of haze. This opacity made the city lighter, impalpable, almost fragile in front of my eyes. This aesthetic quality won me; I was every day inside a fairy-‐tale. I connected my visual experience with the “Air ” of Shanghai. Here it started “Shanghai, Air and other stories”.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 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".