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Record W2954831180 · doi:10.5296/ijhrs.v9i3.14973

A Welfare Model of Street Vendors: Cases from Denpasar, Bali, Indonesia

2019· article· en· W2954831180 on OpenAlexaff
DESAK PUTU EKA NILAKUSMAWATI, MADE SUSILAWATI, Geoffrey Wall

Bibliographic record

VenueInternational Journal of Human Resource Studies · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBookkeepingWelfareMarital statusWork (physics)BusinessEmpowermentLogistic regressionLabour economicsDemographic economicsEconomicsEconomic growthDemographySociologyFinanceEngineeringMathematics

Abstract

fetched live from OpenAlex

This study determines the socio-economic characteristics of street vendors in Denpasar, Bali, Indonesia and proposes a welfare model to examine their well-being. The results showed that street vendors in Denpasar are mostly male, married, with an average age of 39. Most are recent migrants who rent their housing. The majority sell food and beverages from carts and work almost 8 hours per day. Most are self-employed and may be assisted by family labor. Most do not do bookkeeping for their business activities and many are not registered businesses. Most have little access to capital and do not participate in empowerment programs in the form of education/training in business skills. Binary logistic regression analysis shows that the incomes of street vendors are related positively to marital status, age, employment status, physical facilities, and presence of bookkeeping.

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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.296
Teacher spread0.218 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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