Living Qualities in Urban Village Communities in Kuala Lumpur: Identification of Domains and Indicators of Quality of Life
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".