Fast-Food Frontline: COVID-19 and Working Conditions in Los Angeles 
Bibliographic record
Abstract
The fast-food sector is an integral part of the food sector in Los Angeles, employing 150,000 Angelenos in 2019 and comprising over a third of Los Angeles’s restaurant workers. Fast-Food Frontline: COVID-19 and Working Conditions in Los Angeles is based on 417 surveys and fifteen in-depth interviews with non-managerial fast-food workers in Los Angeles County conducted between June and October 2021. The study finds that fast-food workers in Los Angeles County are at higher risk of contracting COVID-19, in addition to facing difficult work conditions that became more acute during the pandemic. The report provides an in-depth portrait of COVID-19 safety compliance through the lens of fast-food workers themselves, the vast majority of whom are women and workers of color. Among other findings, the report finds that nearly a quarter of fast-food workers contracted COVID-19 in the last eighteen months, and less than half were notified by their employers after they had been exposed to COVID-19. Further, almost two-thirds of workers have experienced wage theft, and well over half have faced health and safety hazards on the job, amounting to injuries for 43% of workers. Researchers emphasize the urgency of implementing public policy solutions that are tailored to fast-food workers’ needs and strengthen fast-food workers’ voice in their industry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.013 |
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".