Comparative Analysis of Waqf Generosity Index (WGI) in Generation Y and Z
Bibliographic record
Abstract
As the Islamic endowment (Waqf) has been practiced throughout the world, it is a challenge to measure the level of the generosity index on its practice. In fact, the awareness in performing Waqf among Muslims especially in the Generations Y and Z are important since it is valuable to the Muslims and concurrently can help those needy people. The study objective is to develop the generosity index among the two generations’ classifications. The variables that have been selected in this study namely trust, altruism, personal characteristics, self-image, religious value, psychological benefits, social norms, personal satisfaction and commitment. Total of 450 questionnaires managed to be collected from Generations Y and Z respondents in Klang Valley area. Rasch Measurement Model was employed to analyze the data. The construct was found reliable with Cronbach Coefficient Alpha of 0.94. The statistical indicators such Point Measure Correlation (PtMea Corr) and Person Item Distribution Map (PIDM) revealed that all the two generations have different results for their generosity index. This can be relating where each generation having their own behavior traits in attitudes and intentions. The implication of this paper provides evidence of the usefulness of Rasch Measurement Model in identifying the Generations Y and Z generosity index on Waqf or named as Waqf Generosity Index (WGI).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".