The everyday female entrepreneur and the pursuit of emancipation
Bibliographic record
Abstract
Purpose This study takes an “entrepreneurship as emancipation” perspective to study entrepreneurs defined as “others” on multiple categories: women entrepreneurs whose ventures are necessity-based, bootstrapped and located in economically impoverished areas (neighborhoods) in two Latin-American countries: Chile and Peru. Design/methodology/approach The study takes an interpretivist research approach and analyses inductively interviews with women entrepreneurs. Findings The findings reveal how everyday practices in pursuit of emancipation – while conducted within the existing patriarchal social structure – push the boundaries and contribute to changes in the social system via a variety of outcomes such as intergenerational social mobility, personal fulfilment and strengthening the communities in which the women entrepreneurs operate. Furthermore, while the authors find that in the particular Latin-American context under study, entrepreneuring activities become an emancipatory possibility for the everyday women entrepreneurs, they also highlight a “dark side” of their emancipatory projects. Originality/value The study contributes to recent critical studies in entrepreneurship by demonstrating the diversity and importance of the “mundane” activities undertaken by “necessity-based” entrepreneurs, and the significant – yet underappreciated – reach of their ventures’ impact on issues well beyond economic considerations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".