Bibliographic record
Abstract
What does it mean to study, interpret, and understand Vermeer today, and how do times, perspectives, theories, biographies, and histories act to shape interpretations of canonical art? Is Vermeer a "given" stable and recuperable figure, or someone, something, "produced" by the needs, preoccupations, and pressures of any given historical moment? These were some of the questions posed by a group of scholars who came together in March 2004 for a transdisciplinary colloquium organized and sponsored by the Clark and the AHRB Centre for Cultural Analysis, Theory, and History at the University of Leeds. This colloquium was convened by Nanette Salomon, professor at the College of Staten Island, City University of New York, and Griselda Pollock, from the Centre for Cultural Analysis, Theory, and History at the University of Leeds.. \n \nParticipants included: Ernst van Alphen (University of Leiden), Mieke Bal, (cultural analyst, Amsterdam and Toronto), Clare Pajaczkowska (psychoanalytical cultural historian, London), Alison Rowley (University of Leeds), and Edward Snow ( Rice 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".