“It’s a mixture of emotions”: Nail technicians’ visual storytelling of work and health
Bibliographic record
Abstract
Nail technicians are artists and storytellers. Adapting the arts-based health research (ABHR) methodology of body-map storytelling (Gastaldo et al., 2018) and in partnership with the Parkdale Queen West Community Health Centre, 19 Toronto-based nail technicians of varying levels of expertise visualized their reflections on their work and health on life-sized body-maps. Rather than a harm-centered narrative common to some occupational health work, their embodied and experiential knowledges center joys, strengths, pains, stressors, supports, and hopes. Participants’ narratives highlight multiple layers of emotion—in the framing of their work experiences, in their labor as beauty service workers, and in their body-map creation processes. In addition, body-maps have the potential to evoke empathy in audiences and observers. Nail technicians’ stories extend narratives of health and wellbeing beyond the worksite, as their work conditions and experiences are consequential to other aspects of their lives, such as their social health. As a counter-hegemonic, justice-oriented, and community-generated approach, body-map storytelling and related ABHR approaches can upend knowledge hierarchies, centering the perspectives—and, particularly, emotional knowledges—of nail technicians from racialized, newcomer, and immigrant communities.
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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 | 0.007 | 0.013 |
| 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 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".