Bibliographic record
Abstract
This article describes one collaborative arts-based research project.Portrait artist Mark Gilbert considers lessons for art and healing from one patient, John, whose cancer and portraiture experiences illuminate features of ethical and aesthetic significance about what it means to witness-to regard another's difficult health and health care experiences.Portraiture in Clinical Settings As an artist-in-residence working with patients and caregivers, I became acutely aware of how relationships and interactions between subjects and me generated the process of making portraits.These relationships and interactions inspired me to further my artsbased research in another project, The Experience of Portraiture in a Clinical Setting (EPICS). 1 EPICS sought to explore artistic interactions and shared experiences of such interactions when I painted portraits of patients from the Head and Neck Cancer Clinic at the University of Nebraska Medical Center.The portrait, John, enables investigation of ethical and aesthetic roles of portraiture in John's experiences of navigating depression and cancer.Something Beneficial From the beginning of the first session, John was demonstrative and spoke a great deal, especially during our breaks.He seemed enthusiastic: "I feel like we are doing something beneficial."Yet, it seemed to take everything he had for him to show up.Often, he felt too depressed to sit for me in the studio.When he did, however, he was frank and open about his experiences.He told me he suffered from depression, which was exacerbated by the isolation he felt following his cancer diagnosis.He lamented that, even with a solid support system of family and caregivers, he often felt extremely alone.He described cancer as "a devious, insidious disease" and the radiation treatment as "a very difficult experience" of having teeth pulled, dry mouth, and "messed up" taste buds.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".