Expanding Protected Areas Globally Post-2020: A Critical Perspective from Thailand, with Implications for Community Forestry
Bibliographic record
Abstract
The Convention on Biological Diversity’s post-2020 Kunming-Montreal Global Biodiversity Framework (GBF) includes targets to formally protect thirty percent of Earth by 2030 and stimulate financialization of biodiversity conservation. This paper foregrounds potential risks and limitations of the GBF in Thailand. It examines the historical context of state-run protected areas, including their role in facilitating state territorialization and dispossession, political and cultural persecution, and deleterious economic agendas. It then shows that implementation in Thailand could displace residents of more than 200 villages and supplant roughly forty percent (3,951 square kilometers) of all state-registered community forest lands, which provide various livelihood, cultural, and conservation benefits. The paper challenges three assumptions: that parties to the Convention will recognize the rights and agency of Indigenous Peoples and local communities; that state-run protected areas are managed for biodiversity conservation and not for economic growth; and that perpetual economic growth and modernization are compatible with conservation. Effective and equitable conservation in Thailand and elsewhere requires more socially and ecologically responsive community rights-based approaches that empower (rather than supersede) customary institutions and transcend unsustainable political-economic imperatives for privatization and perpetual economic growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".