It Takes a Rooted Village: Networked Resistance, Connected Communities, and Adaptive Responses to Forest Tenure Reform in Northern Thailand
Bibliographic record
Abstract
Conflicts persist between forest dwelling communities and advocates of forest conservation. In Thailand, a community forestry bill and national park expansion initiatives leave little space for communities. The article analyzes the case of the predominantly ethnic Black Lahu village of Huai Lu Luang in Chiang Rai province that has resisted the threats posed by a community forestry bill and a proposed national park. The villagers reside on a national forest reserve and have no de jure rights to the land. This article argues, however, that through its network rooted in place and connected to an assemblage of civil society, local government, and NGOs, Huai Lu Luang has been able to stall efforts by the Thai government that would detrimentally impact their use of and access to forest resources. Their resistance is best understood not in isolation – as one victimized community resisting threats to their livelihoods – but in connection to place, through dynamic assemblages. A ‘rooted’ networks approach follows the connections and nodes of Huai Lu Luang’s network that influence and aid the village’s attempts to resist forest tenure reform.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".