Occupational justice in sociotechnical contexts: Exploring immigrant platform workers’ experiences of doing, being, becoming, and belonging
Bibliographic record
Abstract
Grounded in occupational justice and sociotechnical perspectives, this focused ethnographic study explored how immigrant platform workers construct meaning through diverse forms of occupational engagement—doing, being, becoming, and belonging—within sociotechnical contexts. Drawing on interviews with 30 immigrant platform workers in Vancouver, Canada, the study highlights the heterogeneity of platform labour, moving beyond commonly studied sectors such as ride-hailing and food delivery to challenge conventional narratives that frame platform-mediated employment solely as income-generating activity. The findings, organized into four interrelated themes, provide an in-depth account of how workers engage in, make sense of, and reconfigure their occupations within sociotechnically mediated contexts. The first theme, Doing Within and Beyond Sociotechnical Boundaries, examines how participants’ everyday occupations are shaped by both the technical demands of platforms and the social negotiations required to maintain client relationships, reputations, and relevance. The second theme, Being at the Edge of Visibility, explores how the interplay of social and technical systems renders workers simultaneously visible, through metrics, ratings, and platform profiles; and invisible, through lack of recognition and relational connection. The third theme, Boundless Becoming, reveals the fluid and aspirational nature of participants’ occupational trajectories, shaped by transnational opportunities and sociotechnical structures. Finally, Belonging Beyond the Bubble highlights how these workers cultivate inclusion within platform-specific communities while navigating broader structures of marginalization. This paper contributes to a more inclusive understanding of occupational engagement in platform-mediated labor, emphasizing the importance of supporting diverse occupational needs, rights, and aspirations beyond economic outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".