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Record W2889332665 · doi:10.1177/1042258718779586

African Business Groups: How Does Group Affiliation Improve SMEs’ Export Intensity?

2018· article· en· W2889332665 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

VenueEntrepreneurship Theory and Practice · 2018
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsBusinessImperfectCorporate governanceEmerging marketsIndustrial organizationSurvey data collectionCommerceFinance

Abstract

fetched live from OpenAlex

Can small and medium-sized enterprises (SMEs) in sub-Saharan Africa (SSA) overcome market imperfections to get the resources needed for exporting? We hypothesize that in many emerging economies, domestically owned SMEs address the hurdle of imperfect markets by creating private governance systems in the form of long-term business relationships in business groups (BGs). Our data are collected from the World Bank’s Enterprise Survey and comprises 8,885 SMEs in 33 SSA countries. We find that the export intensity of BG-affiliated SMEs is superior to independent firms, and that financial, human, and technological resources mediate the intensity of the BG affiliation–export relationship.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.230
Teacher spread0.217 · 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