Navigating occupational balance and identity in the platform economy: Perspectives from immigrant workers
Bibliographic record
Abstract
Gig work, or platform work, refers to short-term jobs acquired through digital platforms and constitutes a growing share of Canada’s economy (Jeon et al., Citation2021). Compared to Canadian-born individuals, immigrants make up a higher percentage of workers in the gig economy (Statistics Canada, Citation2024). There is a literature gap concerning how immigrants’ engagement in the gig economy shapes their occupational identity and sense of occupational balance. This study explores the experiences of immigrants working in the gig economy to develop an understanding of gig work through a perspective centering occupational, rather than economic, perspectives that are dominant in the literature. Using an instrumental case study approach, a secondary thematic analysis of 10 qualitative interviews with immigrants currently employed in different forms of gig work in Metro Vancouver, Canada was completed. Findings highlighted three main themes. First, a balancing act illustrates ways that gig work is often balanced with other occupations in participants’ lives given the flexibility and autonomy it offers. Second, shifting identities addresses how gig work helped participants navigate shifts to their identities following their immigration. Third, beyond the dollar discusses participants’ motivations for engaging in gig work beyond the income it generates. Gig work is nuanced and people’s motivations to engage in this type of employment are diverse. As immigrants experience a shift in their occupational identity and balance a variety of occupations, they may be drawn to this form of employment due to its unique characteristics and perceived benefits given other challenges they may face during their economic integration.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".