MétaCan
Menu
Back to cohort
Record W2904294625 · doi:10.5539/ijef.v11n1p37

Entrepreneurship and Economic Performance in Africa: A Sectoral Analysis with Focus on the Role of Finance, Institutions and Globalization

2018· article· en· W2904294625 on OpenAlex
John Bosco Nnyanzi, Bruno L. Yawe, John Ddumba-Ssentamu

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

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipNexus (standard)GlobalizationAgricultureEconomicsTertiary sector of the economyConditionalityBusinessEconomyFinanceMarket economyPoliticsPolitical science

Abstract

fetched live from OpenAlex

The main aim of the paper was to investigate the role of entrepreneurship on economic performance but with focus on sector-wide growth in 12 selected African countries during the period 2006-2016. Overall, the results suggest that while the quantitative impact of entrepreneurship on economic growth is positively significant, there is a differential effect on the sectors. The service sector in particular is associated positively with entrepreneurship whereas there is no evidence in the data that the growth in the manufacturing and agriculture sectors is influenced by entrepreneurship activities. A further analysis that includes interactions in the model supports the conditionality hypothesis that globalization as well as the quality of institutions and financial development matter in the entrepreneurship-growth nexus. In addition, while internet access and government consumption appear beneficial for the manufacturing and service sectors, the role of personal remittances is observed important for the agriculture sector contribution to GDP whereas trade in services matters for each sector but most significantly in the latter sector. In light of the findings policy recommendations are suggested.

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)0.000
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 score0.621
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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