Bibliographic record
Abstract
Ride curated this David Rokeby retrospective exploring the interdisciplinary focus of an artist whose work draws significantly on the science of computer intelligence. Ride also produced the online catalogue and wrote the introductory essay. Ride’s curation aimed to bring together a representative sample of the work of David Rokeby, a leading international media arts\n\npractitioner whose work has been rarely seen in the UK, and to present it in a number of contexts, including major shows in\n\nLiverpool and Glasgow. Ride aimed to show Rokeby’s work as a contemporary visual artist; to illustrate how the history of ‘new\n\nmedia as art’ can be indicated through a concerted look at a singular artist’s output; and to show how key concepts are\n\ndeveloped by the artist across his body of work.\n\nBeing the first retrospective devoted to Rokeby outside his home country, Canada,\n\nRide’s primary curatorial objective was to address the issue of adequately representing an artist’s creative trajectory through a\n\nretrospective. There are few historical exhibitions of new media work due to problems of hardware obsolescence, therefore\n\ninstallations have to be re-created and re-contextualised for a contemporary audience both through supporting information and\n\ngallery installation.\n\nAnother curatorial objective was to find ways to contextualise the work through a variety of approaches so that it appeals to a\n\nwide range of inter-disciplines. For example Rokeby’s work explores computer intelligence, how computers can ‘see’, ‘speak’\n\nand communicate’ together as a ‘social’ network. Areas of interest include language and linguistics, music composition,\n\ncognitive science and visual culture. Each exhibition emphasises a different aspect of his work with different works included. An\n\nonline catalogue (including 2000 word curatorial essay by Ride) revealed how the exhibition evolved in different venues.\n\nThe project was developed over four years through close work with the artist and supported with £45,000 funds from Canada\n\nHouse, Canadian High Commission and ACE.\n\nReviews include Literary and Linguistic Computing; Furtherfield; The List.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.121 | 0.034 |
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".