Maqasid Approach In Measuring Quality Of Life (QoL)
Bibliographic record
Abstract
Studies and discussion in measuring the quality of life (QoL) has been at the centre stage ever since people realize its importance for the wellness of mankind. It is even more important when the concept is associated with sustainable development goal (SDG). Some studies relate QoL with physical health, psychological health, social relationship and environment. Others associate it with authentic happiness that refers to the criteria of pleasant life, a good life, a meaningful life and balanced life. Nevertheless, what can be considered as a holistic QoL? Can it be confined to the material and physical wellness of the people? Could there be any different perspective in looking at the concept in a holistic view? This study would attempt to answer the above questions by deliberating the dimensions of QoL enshrined by the maqasid al-shariah (the objectives of shariah). External Desk Study is employed in getting the required information and data relating to the definition and measurement of QoL. Relevant literatures in the area are reviewed and analysed in coming up with the new dimensions of holistic QoL. In analyzing the data, deductive approach is used to come up with the list of indicators for every dimension of maqasid-based QoL. Finding of this study is expected to shed light in looking into a much broader dimension of QoL.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".