Literacy Development Through Collaborative Governance in Indonesia: An AHP Based Analysis of the Kampus Mengajar Program
Bibliographic record
Abstract
The literacy rate in Indonesia is still declining due to the COVID-19 pandemic, causing Indonesia to rank low among all countries.Various policies implemented by the government have been carried out, one of which is the Kampus Mengajar program.The Kampus Mengajar program is one of the programs from the Directorate General of Higher Education, Ministry of Education of the Republic of Indonesia, which collaborates with various institutions, including private entities that participate in the target schools.This research discusses the determination of intervention strategies for collaboration in the Kampus Mengajar program in realizing impactful literacy improvement.The drafting process uses the theory of collaborative governance with the Analytical Hierarchy Process (AHP) model.Each stakeholder who becomes a respondent has the authority to choose priorities deemed important in establishing collaboration for the implementation of the Kampus Mengajar program to achieve literacy improvement in Indonesia.The results of this study indicate that, in collaboration for literacy improvement, stakeholders determine that building trust and commitment is the main priority in the collaboration.In addition, strategies for collaboration with both the government and the private sector have also been developed for literacy improvement.The Kampus Mengajar program has demonstrated consistent improvements in literacy learning across its successive implementations.The strategy designed in this research is expected to be used as a guideline for the collaborative-based literacy improvement process that can be implemented in every region.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".