Bibliographic record
Abstract
The use of new technologies has almost inevitably led to the blurring of established definitions, roles, and taxonomies of visual art. The ‘video painting’ is a new form of contemporary video expression based around the latest developments in High Definition Video and flatscreen displays providing a high-quality platform for the presentation of the moving image. It is a hybrid concept between the still and the moving image using traditional patterns of film narration and painting practice, quoting different genres such as the still life, landscape, portrait or the abstract painting. As Jim Bizzocchi of Simon Fraser University in Canada suggests, ‘It is a smooth temporal flow, always changing, but never too quickly. The piece is an exploration of concepts of ambience, time and the liminality of image and of narrative’. Importantly, one of the most interesting questions it poses is with regard to how time is performed in these video paintings. As an example, I present my project ‘Still life in motion’, which I created in 2005 as a German media artist in cooperation with SONY Germany as part of the SONY BRAVIAmotionart project. The canvas is replaced by a large high-resolution flatscreen expanded by perspectives of time and space, simultaneously reconstructing and deconstructing the issues of the still life genre. Other examples will discuss video works by artists such as Bill Viola, Robert Wilson, Sam Taylor Wood, etc., who have downplayed the temporal nature of their images so much, that they often become nearly static in their effect.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".