Strengthening Community Participation in Spatial Planning of Riverflow Regions in Medan City
Bibliographic record
Abstract
Due to the impact of the flooding issues in Medan City, which are driven by the occurrence of sedimentation, narrowing, and law enforcement, it is required to offer alternate options for watershed management through a strategy to increase community involvement in spatial design. This study uses a qualitative, descriptive methodology. Techniques for gathering data include documentation, holding FGDs with seven stakeholders, and conducting in-depth interviews with riverside communities. According to the study's findings, Medan City's watershed management has not been carried out to its full potential through community participation. The situation gets worse as a result, as evidenced by the growth of riverbank communities, deteriorating water quality, poor health, and extensive floods. The primary issue that has to be fixed is the lack of communication between the community and the government. Low public knowledge and ineffective law enforcement in policing and securing river borders are barriers to community participation in spatial planning. This study suggests that pertinent organizations set up a watershed management coordination forum and educate and engage the local population in watershed management. This study offers a thorough understanding of how the quantity and quality of watersheds in Medan City are worsened by poor community participation.
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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 | 0.005 | 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 | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".