Envisioning better forest transitions: A review of recent forest transition scholarship
Bibliographic record
Abstract
Forest transition theory, as introduced by Alexander Mather, depicts forest recovery patterns often occurring in the wake of agricultural intensification and farmland abandonment. Since the forest transition theory was introduced, multiple pathways have been described in the scholarly literature to explain forest transition phases via varied socio-economic forces. This analysis of a set of 78 country-specific case studies published from 2019 to 2022 confirms social inequity in documented forest transitions; forest transition case studies from 2019 to 2022 were concentrated in highly developed countries. This review also substantiates the impact of agricultural land use changes in recent forest transitions. Four out of five case studies assessing pathways identified an economic development pathway for forest transitions. The effect of state interventions such as introducing incentives for reforestation in forest transitions reviewed was mixed; while almost one-third of forest transitions were attributed to state policies or laws, negative biodiversity impacts from forest plantations were documented. With respect to social justice, nearly a third of case studies included interviews with villagers or similar methodologies to capture social perceptions of forest transitions. Based on this review, governance and social equity forest transition benefits are critical issues for forest transition 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".