Large intact forest landscapes and inclusive conservation: a political ecological perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it