Bibliographic record
Abstract
Hybrid artist-scientists are now fairly common. It wasn’t always thus. Certainly music has a relatively long history of cross-fertilization with science, not least because of its obvious mathematical qualities, but also because of the medium-term relationship between music and technology. In formal music studies though, the medium of music was generally considered indivisible from itself, even as mathematical models were used to justify certain theories. Film also has a similar, if somewhat less precisely formalized history, as evidenced by the long history of montage film and visual music. Other fine arts have had less clear relationships with science. This can no longer be said to be the case. Artists are collaborating with biologists, computer scientists, geographers and researchers from other far-flung disciplines. Similarly scientists are learning the value that artists can bring to a project in terms of creativity and “ways of seeing” (Berger, 1972, 1). On-going discipline-centric resistance based on adherence to traditional barriers between the (subjective) arts and the (objective) sciences continues to be prevalent; however, it is fair to say that the gulf between art and science that has widened since the Enlightenment has now been widely challenged by a body of scholars, artists and scientists.©Journal of Professional Communication, all rights reserved.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".