My Art, My Health: Creating empathy through sculpture, Otto Kamensek in dialogue with Jacqueline Davidson
Bibliographic record
Abstract
This paper offers a personal discussion of the use of art, sculpture specifically, to bridge personal experiences with chronic illness: juvenile arthritis. Drawing on the experiences of making work and sharing this work in different contexts, it focuses on what it is like to live with arthritis and how creative practice can be used to benefit both artist and viewers. The works discussed in the piece have been created to explore and develop understanding around issues that are not easily talked about in either the doctor’s office, or in public settings. The figures generated through this body of work provide a device or platform that allows the artist to look at the disease through the course of his life and in so doing, to adopt a third perspective on this sometimes difficult process. Reflecting on that young boy, that teenager, that young man struggling with arthritis, sometimes effectively, at other times not so, the art-making process and shared products allow the artist a certain freeing, revelatory view capable of reconciling trials and struggles with self-knowledge and empowerment. This work allows the artist “to dig into those dirty emotional corners” and to work through uncomfortable thoughts. These works and the experience of discussing them has been useful in educational contexts, particularly germane to further understanding of creative practices, artworks, and their potential contributions to healthcare-related services and service providers.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".