“ <i>Dona de Si</i> ”: Women’s Empowerment as Affective Economies in Corporate-Sponsored Entrepreneurship Training
Bibliographic record
Abstract
The author examines transnational narratives of women’s empowerment in Brazil, highlighting discourses promoting women’s ability to improve society and their incomes by mobilizing emotions and attributes linked to care responsibilities as savvy feminized skills to be used in micro entrepreneurship. On the basis of a case study through ethnographic research conducted between 2019 and 2022 within a national empowerment program for entrepreneurship training, the author argues that such initiatives are grounded in affective economies increasingly reliant on individualized forms of emotional labor as a central strategy to navigate the Brazilian labor market. By analyzing how emotions circulate within the social space of this corporate-sponsored training program, the author explores the role of affects in constituting power dynamics between those “already empowered” women promoting trainings and those yet to be shaped by the required affects to succeed. Drawing on theories of affective economies and governmentality, this study unravels some complexities of promoting empowerment and financial independence among women in the growing context of labor informality in Brazil. The author concludes by demonstrating how these narratives of empowerment create normative affective standards that rely on women’s emotional engagement with entrepreneurial values, often obscuring the precariousness underpinning their labor conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".