Toxic freedom: how middle-class seasonal fruit pickers perceive and manage agrochemical exposures
Bibliographic record
Abstract
In a global agricultural context that is more chemically dependent than ever, occupational exposure to pesticides typically maps onto entrenched inequalities. Existing research has documented the health hazards of agrochemical exposure facing predominantly low-income, racialized farmworkers. Yet some young middle-class people in wealthy countries are intentionally pursuing informal seasonal farm jobs. How do workers in social positions that typically protect against workplace vulnerability manage the uncertainty of toxic exposures? This study draws on ethnographic observations and in-depth interviews with French, English and Spanish-speaking domestic and international farmworkers in British Columbia, Canada. I identify three pathways by which farmworkers perceive and manage agrochemical exposure: informal bodily evidence, individually managing risks and rationalizing exposure. This article introduces the concept of ‘toxic freedom’ to show how workers may downplay workplace risks by framing pesticide exposure as a reasonable trade-off for personal autonomy, countercultural idealism and temporary youthful adventure. This research underscores why individual-level agricultural health and safety interventions may be limited in protecting workers from harmful agrochemical exposures. Rather, it signals the opportunity for policy interventions such as stronger pesticide regulation, proactive spot inspections, higher penalties for non-compliance, and clearer channels for farmworkers to have a collective democratic voice in the workplace.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".