The Sustainable Livelihood Challenge of REDD+ Implementation in the Philippines
Bibliographic record
Abstract
The forestry sector in the developing world has been continuously challenged by the unsustainability of forest resources and the threat of climate change. Reducing Emissions from Forest Degradation and Deforestation (REDD+) was launched to address the problem, and the Philippines accepted the challenge by undergoing the 10-year phased process. Using the sustainable livelihoods framework, this paper examines the challenges of REDD+ implementation in the Philippines using the case of Southern Leyte REDD+ pilot area and highlights the co-benefits and trade-offs of pilot project activities on the five (5) capital assets. Our findings suggest greater impacts of CBFM on the key indicators of change than REDD+. There is very high association of the natural and financial capital assets with REDD+ pilot project activities, yet financial benefit is short-lived. Local people highly regarded the contribution of assisted natural regeneration and reforestation activities in sequestering carbon, while agroforestry is perceived to sustain agricultural production in the future. The major drawback of REDD+ pilot project activities is that it perpetuates the failures of CBFM initiatives giving little attention to sustainable livelihood objectives. Forest conservation policy like REDD+ as a mechanism for addressing climate change can still be adopted by local communities if livelihood capital assets will be further enhanced.
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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".