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Record W2904294034 · doi:10.1186/s12992-018-0444-8

The health of workers in the global gig economy

2018· article· en· W2904294034 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGlobalization and Health · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsRoyal Bank of CanadaPublic Health OntarioUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Waterloo
KeywordsPrecarityWork (physics)BusinessPrecarious workGig economyPromotion (chess)Public relationsEconomicsPolitical scienceMarket economyEngineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.346
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it