Bibliographic record
Abstract
This paper gives a personal perspective on the application and relevance of generative processes to art production. This view is that of a computer programmer, rather than that of a user of computer programs written (or hardware constructed) by others. The programmer is in the unique position of being able to describe and manipulate abstract processes which may be used as a unique means of artistic expression. This gives a greater amount of freedom to the programmer/artist than is the case when he or she is limited by programmed procedures defined by others. Prior to the development of a formal means of specifying visual and aural events, a concrete machine or set of rules for their manipulation and a means of bringing these representations back into the world as physical events, abstract processes were things to be contemplated but not experienced. Musical and spatial notations employed by artists, engineers and others, in concert with the programming of computing hardware, have opened the way for those who wish to manipulate processes in their artistic practice. In order to focus study and practice in the area of such generative computer art, the Center for Electronic Media Art (CEMA) has been established in Melbourne, Australia. The Center has spawned an international conference series on generative/process-based electronic art called Iteration. The perceived roles of the Center and Iteration conferences are discussed in this paper.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".