Community Perceptions and Willingness to Accept and Execute REDD+ Initiative: The Case of Pugu and Kazimzumbwi Forest Reserves, Tanzania
Bibliographic record
Abstract
The study examined community perceptions and willingness to accept and execute Reduced Emissions from Deforestation and Forest Degradation (REDD+) initiative at Pugu and Kazimzumbwi Forest Reserves (PKFRs) in the course of addressing the overriding problem of climate change. The survey was conducted in two villages’ i.e. Kisarawe and Kazimzumbwi adjacent to PKFRs. A total of 110 respondents were randomly selected with a sampling intensity of 10%. Key informants interview, focus group discussion (FGD) and in-depth interviews using a questionnaire administered to selected community members were the major techniques used in data collection. Regarding community perceptions and acceptability of the REDD+ initiative, the study revealed low level of acceptance (16.2%), which was highly attributed to low level of awareness on the initiative. Poor governance and poor community involvement in REDD+ activities were highly ranked as REDD+ perceived problems. Lack of livelihood options was observed to be constraining factor behind community support to the initiative. The study concludes that, for the success and sustainability of REDD+ initiative at PKFRs, robust livelihood options like training the community on how to make charcoal out of dry leaves are needed to be crafted at the shoes of the community in line with educating the community on the rationality of the initiative in their locality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".