Bibliographic record
Abstract
Revised and corrected by Hubert Damisch, January 2005 B. Let me begin recalling that it is nearly thirty years since I began to be involved publishing your work in English. The very first occasion was when I published a translation of your article on Klee's ‘Equals Infinity' in the Visual Poetics number of 20th Century Studies, in 1976. It was also around then that I read Theory of /cloud/, which has now finally been brought out in translation by the Stanford University Press. In the interval, I published your article on Balzac's Chef d'oeuvre inconnu, ‘The underneaths of painting’, in one of the first issues of Word & Image in 1984, and not long afterwards I had the pleasure of being invited by you and Louis Marin to spend a month as Associate Director of Studies at the École des Hautes Études. Most recently we have coincided again in April 2003 as the two Senior Mellon Fellows at the Canadian Centre for Architecture in Montreal. I took good note at the time of the message enclosed in a fortune cookie that you opened at one of our many meals together over that period. It said, in so many words, ‘you are always ready to tackle the big issues.’
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".