NATIONAL WAQF-BASED FOREST INDEX: TOWARDS ENVIRONMENTAL SUSTAINABILITY AND COMMUNITY EMPOWERMENT THROUGH SHARIA FINANCIAL INNOVATION
Bibliographic record
Abstract
Currently, the existing concepts of waqf management and forest management are still separated. It creates an opportunity to develop the concept of waqf-based forest management. This study aims to develop a National Waqf-based Forest Index (NWFI) as a standardized tool to measure the performance of waqf-based forest management practices. This research is a quantitative study that began with preliminary research and was followed by interviews with seven experts using the Analytical Hierarchy Process (AHP). Based on preliminary research, the index comprises five dimensions: institution (with three aspects), process (with three aspects), system (with three aspects), outcome (with twelve aspects), and impact (with three aspects). It is found in the AHP analysis that the institution and process dimension is considered the most important aspect for waqf-based forests, with an importance score of 0.227, followed by the system and impact dimension (0.197), and the outcome dimension (0.152). Based on the consistency rate, all experts' assessments were consistent. Regarding rater agreement, the outcome dimension and several indicators related to the environment and social facilities achieved the highest level of agreement (W ~0.400–0.500), whereas other dimensions and aspects, such as expert opinions, showed significant dispersion. In conclusion, this waqf-based forest index has the potential to strengthen the role of Islamic finance in supporting sustainable projects by providing the basic practical tool for the waqf-based forest nazhir and policy-makers to assess waqf-based forest management and construct future good nazhir governance. Further research and pilot projects are important to evaluate its effectiveness in measuring waqf-based forest management practically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".