Bibliographic record
Abstract
High-Tech Trash 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. High-Tech Trash speaks to a paradox in contemporary society in which failure is disavowed yet necessary for technological innovation. “Leonard Cohen sang ‘There’s a crack in everything…that’s how the light gets in.’ Here, Carolyn Kane teaches us how to see that light, one crack at a time.” FRED TURNER, author of The Democratic Surround: Multimedia and American Liberalism from World War II to the Psychedelic Sixties “Kane profiles art practices and media discourses that exploit and celebrate, rather than filter or suppress, all kinds of errors and noises. A welcome intervention in a number of discursive fields.” PETER KRAPP, author of Noise Channels: Glitch and Error in Digital Culture “An original work of scholarship that addresses some of the most pervasive phenomena and foundational questions in the contemporary media environment.” ROBERT HARIMAN, coauthor of The Public Image: Photography and Civic Spectatorship CAROLYN L. KANE is Associate Professor of Communication at Ryerson University and author of Chromatic Algorithms: Synthetic Color, Computer Art, and Aesthetics after Code.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.947 | 0.931 |
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; the direct Gemma label and the distilled Codex classifier 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".