Collaborative Governance in Poverty Alleviation in Ngada Regency, East Nusa Tenggara Province, Indonesia
Bibliographic record
Abstract
This research was conducted to describe and analyse the Implementation of Collaborative Governance in poverty alleviation and supporting and inhibiting factors in it. Research locus in Ngada Regency, East Nusa Tenggara Province, Indonesia. This type of research is descriptive qualitative. Data collection techniques used were interviews, observation, FGD, observation and document review. Data validity is tested through data triangulation and data analysis using Data Condensation, data presentation and conclusion drawing. The results showed that Collaborative Governance in poverty alleviation in Ngada Regency, East Nusa Tenggara Province has not fully met the substantial elements of Collaborative Governance according to Deserve which includes network structure, Commitment to a Common Purpose, Trust among the Participants, Governance, and Access to Authority, Distributive Accountability / Responsibility, Information Sharing and Resource Access. The dynamics of collaboration have not yet taken place in the real sense. The Resource, Leadership, Institutional and Cultural Factors are the four factors that influence and inhibit collaboration. Drivers of collaboration include the need for resource sharing, leadership vision on poverty issues, and recognition of potential among stakeholders. Obstacles include resource gaps, less facilitative leadership, no representative institutions and a strong culture of government dominance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".