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Record W4387222259 · doi:10.1177/09713557231201181

Analysis of an Entrepreneurial Ecosystem in Northern Haiti to Stimulate Innovation and Reduce Poverty

2023· article· en· W4387222259 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Entrepreneurship · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsIncubatorEntrepreneurshipPovertySustainable developmentEconomic growthValue (mathematics)Service (business)BusinessPolitical scienceMarketingEconomics

Abstract

fetched live from OpenAlex

In Northern Haiti, a unique experience has been developed and an innovation hub has been realised; the core of this is a City of Knowledge that is articulated around an entrepreneurial university, the Institute of Science, Technology and Graduate Studies of Haiti (ISTEAH). Spread over seven departments in the country, as its name suggests, ISTEAH is a technological university that seeks to put science and technology at the service of development by training citizens, leaders and innovators who can promote the advancement of the country. Resolutely turning towards entrepreneurship, this university is in the process of setting up an entire entrepreneurial ecosystem centred around an incubator-accelerator to create—with students, graduates and young people from around the country—technological and social enterprises to generate value, create wealth and jobs and support sustainable development. In this article, this experiment and its perspectives are analysed in light of the sustainable development goals.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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