Parallel experience: how art and art theory can inform ethics in human research
Bibliographic record
Abstract
Trends in ethical research involving humans emphasise the importance of collaboration, of involving research subjects, alongside the researchers in the construction and implementation of research. This paper will explore parallels derived from another tradition of investigation of the human: art and art theory. An artist's inquiry into the problems of human research will be described, followed by the application of arguments from art theory to research practice. Recently artist Christine Borland has provided examples in which the lack of collaboration in research has caused injustice. Borland's work reflects these ethical dilemmas and questions the procedures and assumptions involved. In most cases the value of subject anonymity is called into question because it reduces the subjects' control over themselves. The application of art theory, which has already considered these problems, helps question and explore the ways in which the subject turned object of artistic or scientific interpretation can maintain some control and dignity.
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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 | 0.049 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.167 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".