Dangerous Currents: Risk and regulation at the interface of medicine and the arts, Association for Medical Humanities Conference 2015
Bibliographic record
Abstract
A member of the Organising Committee for the 2015 AMH conference 'The 2015 Association for Medical Humanities annual conference takes as its theme critical conversations between medicine (including surgery, and encompassing healthcare) and the arts (including the humanities and the liberal social sciences) focused on issues of risk and regulation. Again, we live in a culture that, paradoxically, generates risk (especially in the economic sphere) at the same time as it generates more and more regulation. Our greatest risk is that of environmental degradation, yet we continue to make aggregate lifestyle choices that are creating irreversible environmental damage. Our lifestyle choices – junk food, lack of exercise – are so often at odds with maintaining ‘health’ and medicine’s resources are heavily biased towards curative intervention rather than prevention. Art, too, is, or should be, a risky business. I have nothing against art that pleases but surely the main role of the artist is to subvert, upset and challenge habit and convention to make us ‘think otherwise’. Art in critical conversation with medicine should make us think otherwise about descriptors such as ‘health’ and ‘wellbeing’. Nietzsche (and later Gilles Deleuze) described artists as ‘diagnosticians’ or ‘symptomatologists’ of the body of culture – setting out which symptoms emerge in a culture and how we might treat them. Our most pressing symptoms are environmental degradation, and poverty leading to health issues caused by the 1% phenomenon – that the richest 1% are making obscene amounts of money that do not help to raise quality of life for all because of lack of proper redistribution of wealth. The wide range of performances, drama, lm, conversations and discussion of ideas presented in this conference (from delegates and invited artists, doctors and surgeons) will debate Nietzsche’s notion as they address the conversation between risk and regulation across medicine and the arts' Professor Alan Bleakley President Association for Medical Humanities Emeritus Professor of Medical Education Plymouth University Peninsula School of Medicine Visiting Scholar Wilson Centre University of Toronto
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".