Institutionalizing Community-Based Research in Indonesian Islamic Higher Education: Two Cases from the Sunan Ampel State Islamic University Surabaya and Alauddin State Islamic University Makassar
Bibliographic record
Abstract
The article presents a reflective experience on the institutionalization of community-based research in Indonesian Islamic Higher Education. It comprises two case studies from two different universities, the Sunan Ampel State Islamic University Surabaya and the Alauddin State Islamic University Makassar. Both are the two selected partners within the Supporting Islamic Leadership in Indonesia (SILE)/Local Leadership Development (LLD) Project in partnership between Indonesian Ministry of Religious Affairs and Canadian International Development Agency. The project introduces community-based research as an approach to engage community through Tridharma (Three mandates) of Higher Education. The institutionalization covers various activities from raising awareness, building capacity, to developing institutional policy at a national level. The cases show that the different socio-historical context and political dynamic of each campuses influence the process, challenge and response to the institutionalization. However, both campuses share similar reasons for adopting community-based research from the Islamic perspective, namely that using research as a means of promoting social change is consistent with the Qur’anic principles and the Prophetic tradition.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".