Islamic Credit Card: Are Demographic Factors a Good Indicator?
Bibliographic record
Abstract
This study investigates on the relationship between demographic factors and the usage of Islamic credit card as well as Conventional credit card demonstrates their interdependencies. The debatable issues as been addressed by many authorities not only in terms of the numbers of credit card flooding the nation’s economy, but the amount of transactions that end up with payment default and the numbers of credit card fraud as been recorded which threatened the economy should be seriously focused. Nevertheless the advances and changing habits in purchasing activities significantly contributed the diffusion of credit card as becoming more important and relevant in maintaining the purchasing activities. The study was conducted involving 305 respondents as a sample of study. While 26 items were used for addressing the research questions. Section A of the questionnaire seeks for information concerning the demographic profile of the respondents whilst section B and C that used Likert scale aimed to investigate information related to income and usage of credit card. The results of the study offer certain important managerial implications for the policy makers, finance institutions and the authorities bodies that take controls the credit card activities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".