MAPESA'S Role in Protecting Aceh Historical Site: (Study at Historical sites in Banda Aceh and Aceh Besar)
Bibliographic record
Abstract
The title of this research is "The Role of MAPESA in Protecting Aceh Historical Sites (Study on historical sites in Banda Aceh and Aceh Besar)". This research focuses on the strategies used by the MAPESA Community (Masyarakat Peduli Sejarah Aceh) in introducing Aceh's history to the community, as well as the obstacles faced by the MAPESA community in preserving Aceh's historical heritage. The purpose of this research is to find out what strategies the MAPESA Community uses in introducing Aceh's history to the community as well as the obstacles that mapesa communities face as long as they preserve historical relics in Aceh, especially in Banda Aceh and Aceh Besar. This research uses a qualitative approach using the Laswell Model strategy theory. In order to obtain accurate data and informant, data collection techniques used are observation, interview, documentation, and literature studies to find additional references to the problems studied. The results showed that the strategy used by the MAPESA community in introducing Aceh history is two ways, namely through direct communication (meuseuraya and expedition activities) and through media communication (books and social media). And there are two obstacles that MAPESA faces, namely in terms of delivering messages to the community and also technical obstacles ranging from secretariats, budgets, tools, and resources that are still minimal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".