Evaluating Conservation Assistance Programs in the Anambas Islands Marine Protected Area Using the CIPP Model
Bibliographic record
Abstract
The purpose of establishing a conservation area is to protect and preserve biodiversity and its ecosystem so that people can continue to use it sustainably.Effective management of conservation areas requires the role and participation of the community.Community empowerment efforts must be carried out by conservation area managers, one of which is by distributing aid to the community.This study aims to evaluate and examine the effectiveness of the Program by using the CIPP evaluation model and to identify the constraints and obstacles to its implementation.The CIPP evaluation model is a program evaluation model which was developed by Daniel Stufflebeam and colleagues in the 1960s.CIPP is an acronym for context, input, process and product.CIPP is a decision-focused approach to evaluation and emphasizes the systematic provision of information for program management and operation.The sampling method in this study used a purposive sampling technique with typical case sampling.The author interviewed twelve informants who were competent in providing various information and data required.The research results show that this program has run well and effectively.This is proven by (1) the increase in income of community groups after receiving the assistance program and (2) they are actively involved in supporting marine protected area manager in increasing the effectiveness of their management.The hope is that this program should continue and be further improved as much as possible with a larger and broader scope.Thus, more and more community groups will also feel the positive impact of this program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".