High-Tech Trash : Glitch, Noise, and Aesthetic Failure (Edition 1)
Bibliographic record
Abstract
A free ebook version of this title will be available through Luminos, University of California Press’ Open Access publishing program for monographs. Visit <a href="http://www.luminosoa.org/site/books/10.1525/luminos.83/"><U>www.luminosoa.org</U></a> to learn more.<BR /><BR /><I>High-Tech Trash </I>analyzes creative strategies in glitch, noise, and error to chart the development of an aesthetic paradigm rooted in failure. Carolyn L. Kane explores how technologically influenced creative practices, primarily from the second half of the twentieth and first quarter of the twenty-first centuries, critically offset a broader culture of pervasive risk and discontent. In so doing, she questions how we continue onward, striving to do better and acquire more, despite inevitable disappointment. <I>High-Tech Trash</I> speaks to a paradox in contemporary society in which failure is disavowed yet necessary for technological innovation.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 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; both teacher heads agree on what is shown here.
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".