The Axiology of Da'wah Science: Islamic Community Development (PMI) in Supporting Social Workers
Bibliographic record
Abstract
This research explores the axiological aspects of Islamic Da'wah and its role in supporting social workers in the context of Islamic Community Development (PMI). Utilizing qualitative methods and interviews, the study uncovers how Islamic Da'wah's values and ethics form a robust moral foundation for PMI's social work. Core values like justice, compassion, wisdom, and integrity guide PMI's approach, addressing social inequalities, fostering a caring community through empathy, making wise decisions, and upholding ethical standards. This axiological framework empowers PMI to engage in high-integrity social work, positively impacting the Muslim community and fostering sustainable change. Additionally, social work principles such as acceptance, communication, individualization, participation, confidentiality, and self-awareness underpin their practices. Integrating religion and spirituality into social work is essential, with Islam emphasizing collective and individual responsibilities for communal welfare. Muslim social workers apply Islamic concepts like prayer, almsgiving, fasting, and pilgrimage in their work, creating an effective and ethical framework. Within the Muslim context, integrating fundamental Islamic concepts into social work is vital. The principle of tawhid emphasizes respecting all humans as creations of Allah, regardless of background. Zakat highlights the need to assist the poor equitably, fasting promotes empathy, and the pilgrimage underscores global solidarity. By applying these principles, Muslim social workers offer holistic services grounded in Islamic values, enhancing individuals' and communities' quality of life within the broader Muslim society.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".