MétaCan
Menu
Back to cohort
Record W1512067181 · doi:10.5539/ibr.v8n5p120

The Conception of Street Vending Business (SVB) in Income Poverty Reduction in Tanzania

2015· article· en· W1512067181 on OpenAlexvenueno aff
Nasibu Rajabu Mramba

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaPovertyPoverty reductionInformal sectorBusinessPoverty trapPoor peoplePsychological interventionEvasion (ethics)EconomicsEconomic growthSocioeconomics

Abstract

fetched live from OpenAlex

Street vending is ubiquitous, especially in developing countries. Despite its role in pro-poor economies, it has received little attention; much has been focused on its negative impacts like, use of public space, congestion, health and safety risks, tax evasion and the sale of shoddy merchandise. In Tanzania, street traders are usually concerned with confrontation with local authorities, and at the end they lose their products and money. This study is basically concerned with exploring the approaches of street vending business operation and conceptualizes the best mode of operation for successful income poverty reduction. It is carried out by reviewing previous studies relating to SVB and the micro informal business sector in general. The study proposes a model for street vending business operations and its interventions that can lead to an income poverty reduction.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.106
GPT teacher head0.346
Teacher spread0.241 · 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 designQualitative
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

Citations48
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicMicrofinance and Financial InclusionFrench-language works237,207