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Record W2085804524 · doi:10.1002/nml.159

To profit or not to profit: Women entrepreneurs in India

2007· article· en· W2085804524 on OpenAlex
Femida Handy, Bhagyashree Ranade, Meenaz Kassam

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNonprofit Management and Leadership · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCasteWomen entrepreneursProfit (economics)Stochastic gameFor profitNot for profitEntrepreneurshipLabour economicsBusinessEconomicsDemographic economicsEconomic growthPublic relationsMicroeconomicsPolitical scienceClassical economicsFinanceLaw

Abstract

fetched live from OpenAlex

Abstract Entrepreneurial activity attracts certain kinds of individuals, whether it is to promote a social cause in the nonprofit sector or profit in the for‐profit sector. This article looks at the behavior of women entrepreneurs in India in both the for‐profit and nonprofit sectors to test for potential differences and similarities. We chose two groups of entrepreneurial women who founded and led relatively similar‐size organizations in the same city and who provided services primarily to women and children. Our findings show that while all nonprofit entrepreneurs receive a high payoff from promoting social causes, there is no single unifying payoff for for‐profit entrepreneurs. Family background and support, however, play an important role for both sets of entrepreneurs. We find that experience in the sector, social class, caste, and education in?uence entrepreneurial behavior and that this in?uence differs by sector.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.071
GPT teacher head0.274
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it