MétaCan
Menu
Back to cohort
Record W7128494427 · doi:10.64903/1480-6800.22.4.298

Living Qualities in Urban Village Communities in Kuala Lumpur: Identification of Domains and Indicators of Quality of Life

2019· article· W7128494427 on OpenAlexvenueno aff
Muhammad Syamil Mohd Shamsul, Norhaslina Hassan, Safiah Yusmah Muhammad Yusoff, Amirhosein Ghaffarianhoseini

Bibliographic record

VenueArab world geographer · 2019
Typearticle
Language
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsKuala lumpurUrbanizationIdentification (biology)Urban villageQuality of life (healthcare)

Abstract

fetched live from OpenAlex

Urbanisation has serious impacts on many challenges associated with quality of life. In Kuala Lumpur, the pace of urbanisation has led to some change, resulting in some villages becoming a formal part of the city. Research on facets of the quality of life – what we term here ‘living qualities’ – in urban village communities is important in understanding the impact of urban development on these marginalised communities. This paper aims to identify the domains and indicators for such living qualities in urban village communities in Kuala Lumpur through in-depth analysis and synthesis, investigating the respective domains and indicators as also supported by relevant policies in Malaysia. The study provides validation based on expert consensus to further strengthen the justification for the domains and indicators selected. Based on an extensive review of related relevant research on living qualities and validation by experts, nine domains and 45 indicators have been identified with a CVR (content validity ratio) of 0.62 or higher and a CVI (content validity index) of 0.99. The findings can be utilised as a basis for assessing facets of the quality of life in urban village communities in Kuala Lumpur and as a whole.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.316
Teacher spread0.293 · 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 teacher head, not a consensus.

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueArab world geographerSame topicPsychological Well-being and Life SatisfactionFrench-language works237,207