Bibliographic record
Abstract
The Video Art of Sylvia Safdie brings into focus the complete video oeuvre of a pioneering Canadian artist. Tracing the development of Safdie’s work and its implications for the future of media art, this volume provides a stunning perspective on her videos and sets a new standard for the presentation of video art in book form. Safdie's principal video works are presented in the form of more than 200 images, selected and arranged to suggest the content, rhythm, and movement of the videos themselves. Alongside the rich illustrations, the book explores Safdie's video art through a thoughtful introduction to the artist and two insightful critical essays. Eric Lewis relates her videos to her works in other media, considers how she poses key questions in the philosophy of art, and addresses issues concerning Jewish art and identity. He discusses the complex relationship between Safdie's video images and the improvised music she often employs as soundtracks. An essay by music scholar and conductor Eleanor Stubley explores the relationship between the body and mind in Safdie’s videos, shedding light on the emotive and sensorial qualities of the breathing body. A vibrant appeal to both the eye and the mind, The Video Art of Sylvia Safdie showcases an artist at the vanguard of video and intermedia art and demonstrates how her work is representative of the next stage in artistic explorations of time, change, corporeality, and our place in nature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".