Ubiquity : Photography's Multitudes
Bibliographic record
Abstract
A critical anthology on the widespread use and influence of photography\nFrom its invention to the internet age, photography has been considered universal, pervasive, and omnipresent. This anthology of essays posits how the question of when photography came to be everywhere shapes our understanding of all manner of photographic media. Whether looking at a portrait image on the polished silver surface of the daguerreotype, or a viral image on the reflective glass of the smartphone, the experience of looking at photographs and thinking with photography is inseparable from the idea of ubiquity—that is, the apparent ability to be everywhere at once. While photography’s distribution across cultures today is undeniable, the insidious logics and pervasive myths that have governed its spread demand our critical attention, now more than ever.\nContributors: Kate Palmer Albers (Whittier College), Ariella Aïsha Azoulay (Brown University), Maura Coughlin (Bryant University), Niharika Dinkar (Boise State University), Michelle Henning (University of Liverpool), Jacob W. Lewis (University of Rochester), Mohammadreza Mirzaei (University of California, Santa Barbara), Joseph Moore (independent artist), Derek Conrad Murray (University of California, Santa Cruz), Kyle Parry (University of California, Santa Cruz), Annie Rudd (University of Calgary), Mette Sandbye (University of Copenhagen), Catherine Zuromskis (Rochester Institute of Technology)
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".