Body of Aesthetics: The therapeutic potential of aesthetics
Bibliographic record
Abstract
Realized in response to the loneliness epidemic that we are facing as a society, this body of research seeks to gain a better understanding of the curatorial in the context of care, revealing its potential to positively affect well-being. Body of Aesthetics investigates the multimodality of aesthetic experience, beyond the ocularcentricity of formal aesthetics, to explore the therapeutic potential of aesthetic experiences to combat loneliness. The findings are presented in two parts: (1) a methodology, and (2) a practice. As a methodology, Body of Aesthetics offers a new way to approach curation in the context of care, without being prohibitive or prescriptive. The titular exhibition presents this methodology in practice; serving as a case study, the exhibition demonstrates how curators can support individual and social well-being through aesthetic practices. \n \nThe Body of Aesthetics exhibition features artists Orus Mateo Castaño-Suárez and Artemis Han who respond to the injustices faced by the body as it is reduced to a means of production, and through its subjugation by the medical gaze. Each artist addresses a perceived social or political injustice in contemporary mental health diagnosis and treatment, offering avenues for hope. The accompanying catalogue essay examines these themes further, weaving them into a narrative about a being that has struggled over centuries to resolve itself. Torn asunder by the ego of philosophical and medical hegemony, and isolated by oppressive, modifying punctuation, it embarks on a journey to reconcile and reinvent itself, supported by the concerted effort of contemporary philosophers, anthropologists, and medical practitioners who recognize its plight.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".