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Record W4210316615 · doi:10.32920/19071767

Exploring Entrepreneurial Coaching as a Gendered Phenomenon Within Business Incubators

2022· preprint· en· W4210316615 on OpenAlexaff
Mariah M. Maxheimer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCoachingEntrepreneurshipWomen entrepreneursPerspective (graphical)PhenomenonPsychologyQualitative researchBusinessSociology

Abstract

fetched live from OpenAlex

Business incubators are organizations that promote and support entrepreneurial activity through services such as entrepreneurial coaching. However, how coaching impacts entrepreneurs is not well understood, particularly from a gendered perspective. It is essential to understand this gap because women are underrepresented in the field of entrepreneurship. Gender stereotypes create barriers that portray females as being less capable entrepreneurs. Therefore, women must overcome challenges that their male counterparts do not. The emergent model from this qualitative study of six female entrepreneurs, five male entrepreneurs and four coaches suggests three dimensions of coaching (venture support, emotional support, gender sensitivity) that benefit entrepreneurs. Insights into the differences between the coaching needs of men and women entrepreneurs were obtained by drawing on entrepreneurial self-efficacy to explain the relationship between the dimensions of coaching and entrepreneurial outcomes. The findings suggest implications for future research, incubation management, and public policy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.258
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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