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Record W3109158339 · doi:10.5267/j.msl.2020.11.022

Uncontrolled consumption and life quality of low-income families: A study of three major tribes in south Sulawesi

2020· article· en· W3109158339 on OpenAlexvenueno aff
Hasmin Tamsah, Gunawan Bata Ilyas, Abdul Latief R, Yuswari Nur, Yusriadi Yusriadi, Andi Asrifan

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Sociology, Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyDisadvantagedConsumption (sociology)Low incomeEmpirical researchEconomic growthDevelopment economicsEconomicsBusinessSocioeconomicsDemographic economicsSociologySocial science

Abstract

fetched live from OpenAlex

Poverty analysis has often been necessary to generate new studies and publications. But for all the countries in the world, including Indonesia, poverty remains a concern. Indonesia has diverse concepts of culture, comprising of numerous tribes and traditions. Any empirical findings suggest that culture is closely correlated with customs and behaviors. It is what motivated the authors with a resource-based approach to undertake this study. This paper forms part of a series of documents created since 2010 and added fields and informants in 2018. This paper would include an overview of the behavior trends and improvements in disadvantaged households' quality of life. Besides their low wages, their eating habits are complicated due to their limited capacities. The researchers discusse “uncontrolled consumption” in this article, which exacerbates low-income families with low income. All of this directly impacts their life experience.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

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 studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.125
GPT teacher head0.396
Teacher spread0.271 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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