The trouble with the trees: can community-based forestry succeed in Panama and around the world?
Bibliographic record
Abstract
Community-based forestry (CBF), a forest management strategy in which communities take a more active role in the management of local forests, has expanded quickly around the world since the 1990s.Since communities are thought to have local knowledge and a stake in the longterm sustainability of the resource base, CBF is theoretically positioned to produce more sustainable outcomes for people and the environment.Evaluations of CBF performance, however, have generally found mixed results.For many researchers, enthusiasm is ceding to skepticism, and more research into how CBF performs in a diversity of contexts around the world is needed.This thesis first reviews the literature on CBF's theory and performance before presenting a case study of CBF's current status in Panama.As a developing country in the tropics facing both high rural poverty rates and ongoing deforestation, Panama is viewed as a good candidate for CBF implementation.However, using an evaluative framework developed by Gilmour (2016) finds that the country is not fully ready on political or economic grounds to support successful CBF.CBF policy studies in other countries have reported similar results.Governments are usually reluctant to devolve power and communities often lack the technical capacity to carry out management activities.Noting the persistence of these problems, the next section reflects on the broad trajectory of CBF research.By applying a computational linguistic Thesis style and co-author contributionsThis is a manuscript-based thesis, with Chapters 2 and 3 to be published independently as standalone research papers in scientific journals.This means that there is some repetition across the different sections and references are presented at the end of each chapter.Chapter 1 provides a general introduction, lays out the objectives of the study, and presents a literature review to inform the thesis's conceptual framework.Chapter 2 presents a case study assessing community-based forestry policies in Panama and considering their potential to meet objectives.Chapter 3, building on a gap between research and policy observed in Chapter 2, reflects on the general trajectory of community-based forestry research literature over the past several decades using bibliometric analysis and statistical topic modelling.Chapter 4 concludes the thesis with a general discussion and suggestions for future research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".