Database Aesthetics: Art in the Age of Information Overflow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Database Aesthetics examines the database as cultural and aesthetic form, explaining how artists have participated in network culture by creating data art. The essays in this collection look at how an aesthetic emerges when artists use the vast amounts of available information as their medium. Here, the ways information is ordered and organized become artistic choices, and artists have an essential role in influencing and critiquing the digitization of daily life. Contributors: Sharon Daniel, U of California, Santa Cruz; Steve Deitz, Carleton College; Lynn Hershman Leeson, U of California, Davis; George Legrady, U of California, Santa Barbara; Eduardo Kac, School of the Art Institute of Chicago; Norman Klein, California Institute of the Arts; John Klima; Lev Manovich, U of California, San Diego; Robert F. Nideffer, U of California, Irvine; Nancy Paterson, Ontario College of Art and Design; Christiane Paul, School of Visual Arts in New York; Marko Peljhan, U of California, Santa Barbara; Warren Sack, U of California, Santa Cruz; Bill Seaman, Rhode Island School of Design; Grahame Weinbren, School of Visual Arts, New York. Victoria Vesna is a media artist, and professor and chair of the Department of Design and Media Arts at the University of California, Los Angeles.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it