Bibliographic record
Abstract
This article analyzes nail technicians' occupational health experiences using body and hazard mapping -a visual, low-cost, and worker-centred approach.Thirty-seven Torontobased nail technicians from predominantly Vietnamese, Chinese, and Korean communities identified various occupational illnesses, injuries, and symptoms on visual representations of human bodies (body mapping) and linked these to their hazard sources in the nail salon (hazard mapping).The impacts identified include musculoskeletal aches and pains, stress and mental health concerns, various symptoms linked to chemical exposure, and concerns about cancer and reproductive health.Rather than a conventional occupational health approach, this work draws on Vanessa Agard-Jones' expansion of the "body burden" as more than the bioaccumulation of chemical agents.As such, this article asserts that nail technicians' body burden encompasses various types of occupational illnesses and injuries.In addition, nail technicians are exposed to broader "toxic" systemic inequities and structural conditions that allow these workplace exposures to occur and persist.By illustrating the embodied and experiential knowledges of nail technicians and contextualizing this lived experience, the body and hazard maps illuminate vast layers of harm -or multiscalar toxicities -borne by nail technicians.Moreover, as a group-based method, body and hazard mapping allow collective reflection and can spur worker mobilization toward safer and fairer nail salons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".