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Record W1499028561 · doi:10.3386/w20854

Invisible Women: Entrepreneurship, Innovation and Family Firms in France during Early Industrialization

2015· preprint· en· W1499028561 on OpenAlexaff
B. Zorina Khan

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsBibliothèque et Archives nationales du Québec
FundersNational Science Foundation
KeywordsEntrepreneurshipIndustrialisationFamily businessBusinessEconomicsEconomic geographyBusiness administrationMarket economyFinance

Abstract

fetched live from OpenAlex

Family firms are typically associated with negative characteristics, including lower tendencies towards innovation, a higher risk of failure, and inefficiencies deriving from nepotism among family members, criticisms which are even greater when the company is handed over to a female relative. Women in business have generally been presented as petty traders and passive investors, whose entrepreneurial activities were scarce because of such restrictions as limited human capital, culture, market imperfections, and institutional biases. The French economy has similarly been faulted for the prevalence of family firms during the nineteenth century, and for disincentives for the integration of women in the business sector. These issues are explored using an extensive sample of women who obtained patents and prizes at industrial exhibitions during early industrialization. The empirical evidence indicates that middle-class women in France were extensively engaged in entrepreneurship and innovation, and that their commercial efforts were enhanced by association with family firms. Their formerly invisible achievements suggest a more productive role for family-based enterprises, as a means of incorporating relatively disadvantaged groups into the market economy as managers and entrepreneurs.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.328
GPT teacher head0.406
Teacher spread0.078 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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