Bibliographic record
Abstract
Marshall McLuhan, Canadian professor of English literature once said: “We shape our tools, and then our tools shape us.” As soon as the use of digital tools and processes started in art and design, the creative output began to be influenced by these tools, processes and evolved into a new aesthetics. Computers seem to have very precise and strict rules about how one uses them and this concrete ‘mechanical’ aspect leads to the perception that abstract notions like spontaneity and serendipity cannot exist in the course of digital creation. This view is challenged both by scientists and artists. One of the early and significant efforts is ‘Cybernetic Serendipity’; the first large international exhibition of electronic, cybernetic, and computer art which took place at the Institute of Contemporary Arts (ICA) in London, UK, from 2 August to 20 October 1968. “The title of the exhibition suggested its intent: to make chance discoveries in the course of using cybernetic devices, or, as the Daily Mirror put it at the time, to use computers ‘to find unexpected joys in life and art.’” (Usselmann, 2003). Creativity is stochastic and assumptive in nature. The importance of randomness in the creative process must not be ignored, underestimated or intentionally disregarded in a condescending way. Notions of chance, randomness, or unpredictability are much important, especially when it comes to artistic creation. For instance, artistic movements such as Surrealism and Dadaism “used impossible, incongruent images to provoke unexpected truths and sentiments through metaphor, mistake, absurdity, spontaneity, and serendipity.” (Hinrichs, 1995) This dimension of unexpectedness can be taken to the apparently paradoxical conception of ‘aesthetics of failure’ level; where, be it good or bad, you find accompanying abstract concepts of surprise, luck or chance. These concepts are quite in harmony with the phenomenon of internet, where non-linear navigation is of intrinsic nature. Internet surfing is a fantastic practice of serendipitous discovery, in which getting lost to find an unanticipated result or content is highly typical.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.029 |
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".