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.
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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.043 | 0.066 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".