Theorizing Non-Participation in a Mail-Based Participatory Visual Research Project with 2SLGBTQ+ Youth in Atlantic Canada
Bibliographic record
Abstract
Between July 2020 and July 2021, 50 2SLGBTQ+ youth from across Atlantic Canada received monthly packages in the mail containing: 1) art supplies; and 2) a prompt in a project called Pride/Swell. Through art making and collaborative archiving, Pride/Swell participants shared experiences creating queer-focused community around identities and space during COVID-19. In this chapter, the authors focus on what non-participation has looked like in Pride/Swell. Specifically, the authors wonder: How might facilitators work with participants as they engage (and disengage) in multiple ways in participatory visual research projects conducted at a physical distance? How might researchers adjust their work as facilitators to make space for multiple forms of participation and non-participation in research projects over time? The authors found that providing time, resources, DIY videos, and opportunities to build art practices was useful, even if participants were less willing to share their art pieces with researchers. This awareness, in addition to the recognition that 2SLGBTQ+ individuals have long been denied affirming, intersectional representation within dominant historical narratives can be considered a potential factor in theorizing non-participation in distance and mail-based projects, as well as in art production, and in digital archives.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.038 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".