When Stigma Doesn’t Transfer: Stigma Deflection and Occupational Stratification in the Sharing Economy
Bibliographic record
Abstract
Abstract Research has suggested that when an occupation is stigmatized, new occupational members will assume the stigma of incumbents because stigma transfers. Yet, current research does not account for shifts in the modern workforce that are changing the nature of many stigmatized occupations. We argue that these changes raise questions about whether stigma will transfer to new occupational members. Drawing from a study of Uber’s entry into Toronto, Canada, we reveal the process by which stigma transfer can be avoided by new occupational members. We show how categorical ambiguity during entry enabled two sets of activities: creating categorical distinctiveness and showcasing identity discrepancies. These activities acted as mechanisms of stigma deflection by distancing Uber drivers from the taint associated with taxi drivers. However, this further entrenched the taint facing incumbents and stratified the occupation along a stigma faultline. We offer implications for research on stigma, market entry, and the sharing economy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".