Masculinity and Gig Work: A Case Study of Rideshare Workers in Toronto
Bibliographic record
Abstract
This thesis examines shifting masculinities and platform labour, following eleven semi-structured interviews conducted with male Toronto-based Uber and Lyft rideshare workers with dependents (children). Women have commonly done non-standard work, hence the proliferation of non-standard work being contextualized as the ‘feminization of work’ (Zahn, 2019). In contrast, rideshare work is a non-standard form of gig work done predominantly by men, rendering it a relevant form of platform work to examine with its complicated relationship to the historical context of gender and nonstandard work. This thesis argues for a need to organize the worker as a whole, examining how workers’ unpaid social reproductive labour and balancing of rideshare work, and often another form of paid work, impacts the viability of classic organizing methods. I argue that these issues of convoluted boundaries between paid and unpaid work must be incorporated into the potential organizing demands of a rideshare workers’ union and identify areas for further research on organizing rideshare workers accounting for shifting masculinities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".