“A Space Where People Get It”: A Methodological Reflection of Arts-Informed Community-Based Participatory Research With Nonbinary Youth
Bibliographic record
Abstract
This article is a methodological reflection of Bye Bye Binary, a community-based participatory research project (CBPR) that explored nonbinary youths’ experiences of identity development, engagement in activism, discrimination, and mental health in Ontario, Canada. The arts-informed method of body mapping was employed in a workshop format to garner the experiences of 10 nonbinary youth (aged 16–25), in conjunction with additional qualitative methods (i.e., individual interviews and reflective notes). Findings suggest that the body-mapping workshop fostered a safe environment that promoted idea generation, affirmation, self-exploration, and connections through a shared identity, thus creating “a space where people get it.” Methodological challenges that arose throughout the process are discussed, including engagement in art as “awkward,” barriers of limited time and funding, participant recruitment, and collaboration and integration. Lastly, the authors reflect on their learnings engaging in CBPR and provide insights into how researchers can move forward and apply these methods and processes into their own work engaging in arts-informed research or with nonbinary individuals.
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 | 0.167 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".