Bibliographic record
Abstract
Variable Conditions recovers and explores early Canadian encounters between computational media and contemporary art in the late twentieth century, charting a network of developments linking meteorology, computation, and the arts that arose long before the age of cloud computing. Essays uncover the material conditions that shaped the emergence of computational arts in Canada, from projects executed by mainframe to digital paintings and analog synthesizer performances. A surprising number of institutional circumstances granted access to early computer hardware – government nuclear and hydroelectric infrastructure, agencies as diverse as the National Film Board and the National Research Council, and a myriad of university settings across the country – and creative conditions varied from benign administrative neglect to the artistic exploration of randomness or a distinct emphasis on thematizing transformation as a motor for graphic visualization and auditory exploration. Interviews featuring leading artists give first-hand insight into artistic practices and the historical moment in which they occurred. The book provides valuable new perspectives on computer art pioneers such as Leslie Mezei, Robert Adrian X, Suzanne Duquet, Roger Vilder, and Vera Frenkel, as well as new contexts for understanding Michael Snow and IAIN BAXTER&. Not limiting their explorations to art generated using computers, contributors outline the integration of computational techniques and concepts into artistic methods across disciplines and trace computation’s emergence as a matter of interest and concern for a range of contemporary cultural producers. Combining historical analyses with theoretical approaches to computation and its entanglement with contemporary cultural discourses and social movements, Variable Conditions excavates the origins of computational arts and, in the process, sketches a new landscape of interdisciplinary creation and surprising connections between scientific and artistic institutions.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.393 | 0.151 |
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".