MétaCan
Menu
Back to cohort
Record W4231227482 · doi:10.14426/ahmr.v3i2.833

Informal Entrepreneurship and Cross-Border Trade between Mozambique and South Africa

2017· article· en· W4231227482 on OpenAlexfundno aff
Abel Chikanda, Inês Raimundo

Bibliographic record

VenueAFRICAN HUMAN MOBILITY REVIEW · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Economic Development and Planning
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsInformal sectorEntrepreneurshipBusinessHospitalitySmall businessSample (material)Goods and servicesFace (sociological concept)Economic growthEconomyEconomicsFinanceTourismGeography

Abstract

fetched live from OpenAlex

Informal cross-border trading is an essential part of Maputo’s informal economy. This paper presents the results of a 2014 SAMP survey of informal entrepreneurs involved in cross-border trade between Johannesburg and Maputo. A questionnaire was administered to a sample of 403 informal traders in 7 markets in Maputo. The study showed that most of the entrepreneurs began their business activities as vendors and only later moved into cross-border trading. The overwhelming majority used their personal savings to start their business and they face significant barriers in accessing business loans from formal banking channels. The study demonstrates the importance of cross-border traders to both the Mozambican and South African economy. In South Africa, the cross-border traders make a significant contribution by buying local goods and utilising the services provided by the country’s travel and hospitality industry. In Mozambique, they supply affordable products to the country’s growing informal sector and play an important role in generating employment.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.065
GPT teacher head0.394
Teacher spread0.329 · 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 designObservational
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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAFRICAN HUMAN MOBILITY REVIEWSame topicLocal Economic Development and PlanningFrench-language works237,207