Using Performative Art to Communicate Research: Dancing Experiences of Psychosis
Bibliographic record
Abstract
This paper highlights a collaborative effort to bring art and science together. In the field of arts-based research, collaboration between social scientists and artists is critical.1Horsfall and Titchen state that “critical creativity as methodology disrupts traditional edges and enables participation of people in the research who are unlikely to engage in philosophical, theoretical and methodological study, but who can understand its assumptions through embodied experience … [It] opens up endless spaces for genuine democratization of knowledge creation” (156). It was this type of democratized space that we wanted to create. We believed that bringing artists and scientists together would contribute to minimizing boundaries that often exist between these two worlds. We found that our collaboration provided a chance for meaningful dialogue and partnership. Additionally, as Jones states, “reaching across disciplines and finding co-producers for our presentations can go a long way in insuring that, rather than amateur productions, our presentations have polish and the ability to reach our intended audiences in an engaging way” (71).
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.026 | 0.055 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".