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Incubators and Accelerators as Illusionary Leaders for Equality in Entrepreneurial Ecosystems

2022· article· en· W4283833707 on OpenAlexaffabout
Tomke Jerena Augustin, Suzanne Gagnon, Wendy Cukier

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEquity (law)EntrepreneurshipDiversity (politics)Position (finance)Power (physics)Entrepreneurial leadershipPublic relationsInclusion (mineral)BusinessSociologyMarketingPolitical scienceSocial science

Abstract

fetched live from OpenAlex

A small but growing body of research investigates issues pertaining equity, diversity and inclusion (EDI) in entrepreneurial ecosystems (EEs). While studies in the broader entrepreneurship literature suggest that barriers for women and underrepresented groups exist profoundly at the early stages of new venture creation, there is limited understanding as to how ecosystem actors and interactions contribute to these. We present findings from a critical discourse analysis of the websites of 176 Canadian business incubators and accelerators (BIAs) that show the effects of a power struggle of these organizations to cater to multiple stakeholders on EDI in the EE. Specifically, we find that BIAs create discursive practices that position them as what we label illusionary ecosystem leaders. While using language to show leadership in addressing entrepreneurs and external stakeholders, they largely follow institutional pressures present in the EE, making their leadership illusionary. We develop a theoretical model of the impact on systemic inequalities of the discursive practices, contribute to the literature on EDI and leadership in EEs, and discuss implications for management.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.273
Teacher spread0.236 · 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 designNot applicable
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

Citations2
Published2022
Admission routes2
Has abstractyes

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