Illicit Resilience: Revisiting Political Ecologies of Conservation Noncompliance in the Context of the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Over the past several decades, the spatial extent of national parks, forest reserves, and other protected areas has expanded rapidly, encompassing more than 16 percent of the Earth’s terrestrial and inland water area in 2024. This expansion of protected areas has often led to population displacements and heightened restrictions on access to land and natural resources for indigenous peoples and other local communities, at times precipitating various conflicts, contestations, and broader forms of noncompliance with conservation regulations. Despite ongoing challenges in resolving trade-offs between conservation and livelihood objectives, the recently established Kunming-Montreal Global Biodiversity Framework (GBF) seeks to achieve even more ambitious conservation targets. These include GBF Target 3—sometimes referred to as the “30 × 30” target—of encompassing 30 percent of all terrestrial and marine ecosystems in protected areas or other effective area-based conservation measures by 2030. The GBF’s vision for achieving Target 3 implicitly assumes, however, that widespread challenges related to noncompliance in conservation governance can largely be resolved in their present manifestation(s), and prevented from reemerging as the scale of the global conservation estate increases. Seeking to deepen our understanding of the emergence and recurrence of illicit or otherwise non-rule-complying behaviors in everyday practices of conservation governance, we draw on a case study of the Manas Biosphere Reserve, a UNESCO Man and Biosphere Reserve in the India-Bhutan borderlands region, northeastern India. Highlighting key findings from this case study, we foreground the concept of illicit resilience to encompass a suite of informal factors and dynamics that lead to the persistence of de facto land uses and management regimes in ways that often strikingly diverge from de jure prescriptions for resource conservation within protected areas. These dynamics include (1) long-term insurgency and political unrest; (2) collusive corruption, patronage, and local politics; and (3) elite-driven processes of informal resettlement and subsequent occupation of forestland. In short, we argue that a deeper understanding of such forms of illicit resilience and their underlying drivers will be crucial for more accurately conceptualizing both feasibility constraints on, and socioenvironmental justice implications of, efforts to rapidly upscale the spatial remit of conservation areas by 2030 and beyond.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".