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Record W4399798157 · doi:10.55482/jcim.2024.33658

Policy Challenges and Opportunities: Migrant Female Entrepreneurs in Northern Europe

2024· article· en· W4399798157 on OpenAlex
Roberto Pessoa de Queiroz Falcão, Victoria Barbosa C. Cunha

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Comparative International Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEntrepreneurshipFlexibility (engineering)ImmigrationSociocultural evolutionWomen entrepreneursEthnic groupPublic policyPolitical scienceEconomic growthBusinessEconomicsManagementFinance

Abstract

fetched live from OpenAlex

The article examines the profound influence of public policies on female migrant entrepreneurship, emphasizing their impact at both local and global levels. Highlighting diverse obstacles faced by female entrepreneurs, including financial constraints, limited knowledge, gender bias, and sociocultural factors, it underscores the pivotal role of governmental support. Specifically, in Northern Europe, gender equality, integration, and entrepreneurship policies are identified as crucial facilitators. Thus, migrant women, facing compounded challenges of gender, ethnicity, and immigration status, encounter barriers to accessing local opportunities. Motivations for entrepreneurship span economic survival, flexibility, and escape from domestic challenges. However, low-tech migrant enterprises often remain overlooked. Women’s business groups and governmental initiatives emerge as vital sources of support, emphasizing the need for tailored policies benefiting female entrepreneurs, especially migrants. The integration of such policies within broader entrepreneurial ecosystems ensures alignment and mutual reinforcement, thus policymakers are urged to recognize and address the distinct needs of female migrant 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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.475

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 head0.117
GPT teacher head0.363
Teacher spread0.245 · 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