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Record W4404931130 · doi:10.3390/jrfm17120544

The Relationship Between Sociodemographic Attributes and Financial Well-Being of Low-Income Urban Families Amid the COVID-19 Pandemic: A Case Study of Malaysia

2024· article· en· W4404931130 on OpenAlexvenueno aff
Abdullah Sallehhuddin Abdullah Salim, Norzarina Md Yatim, Al Mansor Abu Said

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
FundersUniversiti Tunku Abdul RahmanMultimedia University
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakLow incomeBusinessSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Demographic economicsSocioeconomicsEconomic growthGeographyEconomicsMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic and the Movement Control Order (MCO) have had a negative impact on the financial well-being of low-income families in urban areas. This study involved respondents living in the public housing project (PPR) residential areas in Kuala Lumpur—the capital of Malaysia. The key finding is that the financial well-being of low-income urban families was negatively impacted due to the COVID-19 pandemic and the MCO implementation. Furthermore, the impact on the financial well-being of low-income urban families is significantly different in terms of types of families, type and sector of employment, type of home ownership, household monthly income, and education level. Reforms to the financial assistance system and the community empowerment of low-income urban families are necessary to increase the community’s preparedness and resilience in the face of new shocks in the future.

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 score0.035
Threshold uncertainty score0.070

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 studies0.0010.001
Scholarly communication0.0010.001
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 head0.043
GPT teacher head0.277
Teacher spread0.234 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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