001 PP: See me, hear me, heal me: A THEATRE-BASED APPROACH TO ENGAGEMENT, CO-INTENTIONAL DIALOGUE AND MEANING-MAKING
Bibliographic record
Abstract
“Imagine not being able to recognize yourself in the mirror” -see me. “Nobody asked about how it felt when I lost my voice” -hear me. “I will never be the same, even after dozens of surgeries and years of rehabilitation” -heal me. Our interdisciplinary project (seemehearmehealme.com) involves a triadic collaboration of health researchers, patients, and contemporary artists who are working to enhance public awareness and improve the quality of life of people diagnosed with head and neck cancer. Early in the project, we engaged David Diamond, director of “Theatre for Living” (Vancouver, Canada) to help: 1) foster engagement and sense of group identity, and 2) cultivate reflexive, relational awareness supporting authentic collaborative inquiry. Over the course of the 2-day workshop, team members created a series of physical images which offered a platform for open, co-intentional dialogue, and helped to create a foundation for a respectful, team-based collaborative process. Participants shared: This wasn9t the easiest activity for me but it felt genuine, I have confidence that we truly seek to understand each other; Mind blown; I was struck by the openness the workshop fostered and how it broke down barriers; Overwhelmed by the generosity and openness of the participants. The progressive development of group rapport and trust was palpable. Subsequent meetings, qualitative interviews, workshops, studio visits, and regular e-mail communication have supported ongoing sharing of perspectives and experiences, and generation of multiple meaningful, impactful project outcomes, including upcoming international public exhibitions of FLUX: Responding to Experiences of Head and Neck Cancer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.574 | 0.245 |
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".