When Stigma Doesn’t Transfer: Stigma Deflection and Occupational Stratification in the Sharing Economy
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it