Perceptions of COVID-19 related risks by platform-based couriers: An analysis of user comment threads on Reddit
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic increased demand for app-based platform-based couriers, creating job opportunities for individuals who have lost income because of COVID-19. Through various stages of lockdown, courier workers (e.g., delivering for Uber Eats, Amazon Flex, and Lyft) provide an essential service. At the same time, this form of work poses risks for exposure to the SARS-CoV-2 virus as these workers are highly mobile and in contact with many individuals. OBJECTIVE: To explore how platform-based couriers discuss risks associated with their work during periods of high (first wave, second wave, third wave/rise in concerns regarding variants) and low risk during the COVID-19 pandemic, 2020-2021. METHODS: We provide a narrative analysis of user posts (n = 2,866) on Reddit during periods of interest. RESULTS: Our analysis resulted in three central findings. First, we identified changing patterns in discourse as the pandemic went on. Second, we found that the theme of risk prevailed largely in the first wave, with dialogue dominated by tips and asking for advice about how to manage risk. Third, our findings reveal a growing polarization among users during the latter phases of the study. CONCLUSION: Polarization largely focused on acceptance (or not) of public health measures and the nature of their work as independent contractors and the role/responsibility of courier companies to offer protection. Our study is the first to document risks, from the perspectives of anonymous couriers who may be unwilling to share their honest opinions and thoughts through primary data collection where anonymity is not guaranteed.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".