Large intact forest landscapes and inclusive conservation: a political ecological perspective
Bibliographic record
Abstract
Intact Forest Landscapes (IFLs) are global conservation units that aim to combat fragmentation, alteration, degradation, and loss of global forests. ILFs are typically recognized for their biodiversity, carbon storage, protection of hydroecological systems and other ecosystem services. However, IFLs are distinctive among other conservation efforts because they do not immediately prioritize conservation approaches that have goals of alleviating human poverty or improving well-being. The prevailing view is that IFL conservation should engage with ecocentric models of conservation. In this article, we leverage political ecology's analytical attention to power, institutions, identities, and scales to make suggestions on ways in which to integrate biocentric conservation considerations into IFL practices. From a scoping literature review, we found the following areas are especially critical for the future of IFL conservation: (1) prioritizing Indigenous Peoples and Local Communities (IPLC) as actors and beneficiaries of conservation; (2) identifying the value of knowledge integration and co-production for conservation; (3) addressing heterogenous communities and equity impacts, and (4) the need for procedural mechanisms in conservation initiatives that support nesting Indigenous Peoples and Local Communities management and governance in polycentric systems. Furthermore, the development of diagnostic questions of scaling community-based conservation and adaptive strategies beyond their original scope in terms of community definitions, landscape and political context may be beneficial for addressing multi-stakeholder needs, identifying more equitable approaches, sharing strategies and obtaining successful outcomes in IFL conservation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".