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Record W2296879773

Mexican Migrant Entrepreneurial Readiness in Rural Areas of the United States

2011· article· en· W2296879773 on OpenAlexvenueno aff
Frank L. Farmer, Zola K. Moon

Bibliographic record

VenueJournal of rural and community development · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipLatin AmericansWork (physics)Social capitalHuman capitalRural areaAgricultureFemale entrepreneursEconomic growthDemographic economicsBusinessEconomic geographyPolitical scienceSociologyGeographyEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

A significant literature on minority entrepreneurship exists, but comparable literature on Latin American migrants and their role in local community enterprise development is very sparse. This paper is an extension of earlier work focusing on characteristics of recent Mexican migrants to rural and urban areas. Measures of entrepreneurial readiness are examined along with measures of human, social and migration capital. Migrants with previous experience in owning a business provide rural communities with an unrealized pool of talent and experience. This research demonstrates that business owners who migrate are likely to be married, have non-agricultural work experience and other household assets in the form of land and/or properties, and are more likely to be legally documented arrivals. Additionally, these migrants express additional risk-taking behavior, because they are more likely—along with their siblings—to be family pioneers in migrating, but still attached to migration networks through their community or other close, non-parental relatives. These characteristics illustrate entrepreneurial capacity within these migrant communities in rural areas. Keywords: Entrepreneurship, rural communities, migration, general estimating equations

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.262
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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