Picture-Work: How Libraries, Museums, and Stock Agencies Launched a New Image Economy, by Diana Kamin
Bibliographic record
Abstract
Diana Kamin's Picture-Work begins by defining the title term as the labour behind image collections.Kamin extends her definition of picture-workers beyond librarians to curators, catalogers, editors, and even researchers, and her book explores the picture-problems faced by all as they interact with the material and organizational practices of storage and circulation that shape image collections.A senior lecturer in Communication and Media Studies at Fordham University, this is Kamin's first book.It draws on previous research on image organization and classification, community engaged learning, and media policy.The book constructs a media history from the view of the collection workers themselves and illuminates the social practices in three circulating image collections: the Picture Collection at the New York Public Library (NYPL); the Museum of Modern Art (MoMA); and the stock photography agency H. Armstrong Roberts.Kamin begins with a physical collection that eventually faces digitization and asks what mechanical and philosophical problems the circulation of images pose in each case.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".