Biographical Opportunities: How Entrepreneurship Creates Pride in Alterity in Stigmatized Fields
Bibliographic record
Abstract
In this paper we explore the role entrepreneurship plays in influencing the shame of stigmatized work and its impact on actors’ institutional biographies. We do this through an inductive, qualitative study of female and transgender entrepreneurs operating in the sex trade. Our findings reveal that entrepreneurship created biographical opportunities that allowed for the construction of pride in alterity: a process of viewing the distinctiveness or “otherness” of one’s biography as a value rather than a constraint. Pride in alterity shielded these individuals from feeling shamed by stigma associated with the field. Two distinct biographical opportunities allowed for the creation of pride in alterity: altering social position, by positioning as expert and in control; and building relational ties, the creation of supportive and intimate connections. Despite these opportunities, we found that the type of social change involved in the entrepreneurial efforts was an important source of variance. While some of the entrepreneurs were focused on changing how the world perceived sex work, other entrepreneurs were focused on changes to the practices in the field. Our analysis revealed that a focus on macro or micro social change lead to differences in the ways the entrepreneurs used biographical opportunities, and ultimately revealed constraints on the pride they were able to create.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".