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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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; both teacher heads agree on what is shown here.
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".