The health of workers in the global gig economy
Bibliographic record
Abstract
BACKGROUND: The "gig" economy connects consumers with contractors (or workers) through online platform businesses to perform tasks (or "gigs"). This innovation in technology provides businesses and consumers access to low-cost, on-demand labour, but gig workers' experiences are more complex. They have access to very flexible, potentially autonomous work, but also deal with challenges caused by the nature of the work, its precariousness, and their relationships with the platform businesses. Workers in the Global North and South may also experience these challenges very differently. Based on our report "Towards an Understanding of Canadian Workers in the Global Gig Economy", we present a commentary on the implications of a globalized online platform labour market on the health of gig workers in Canada and globally. MAIN BODY: Based on our scoping review of peer and grey literature, we categorized gig worker vulnerabilities in three ways: 1) occupational vulnerabilities, 2) precarity, and 3) platform-based vulnerabilities. Occupational vulnerabilities are connected to the work being performed (e.g. driving a car or computer work) and are not specific to platform labour. Precarity refers to the short-term, contingent nature of the work, characteristics that may be shared with other forms of work. Some examples of precariousness are lack of health insurance, collective bargaining, or career training and promotion. Finally, platform-based vulnerabilities are particular to the way platform labour is structured. These vulnerabilities include worker misclassification, information asymmetries, and the culture of surveillance. We suggest that, together, these vulnerabilities challenge gig workers' right to health. CONCLUSIONS: We propose that the experience of gig workers around the world must be understood in the context of neoliberalism, which has increased both the globalization and precaritization of work. While gig workers share some vulnerabilities, which have important negative consequences on their health, with other workers, the platform-specific vulnerabilities of workers require further inquiry. In particular, the specific health and overall experience of gig workers in different regions of the world - with different labour policies and sociopolitical contexts for work - must be disentangled as workers in the Global North and South experience this work very differently.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".