Re‐encountering resistance: Plantation activism and smallholder production in Thailand and Sarawak, Malaysia
Bibliographic record
Abstract
Abstract: The emergence of social and environmental movements against plantation forestry in Southeast Asia positions rural development against local displacement and environmental degradation. Multi‐scaled NGO networks have been active in promoting the notion that rural people in Southeast Asia uniformly oppose plantation development. There are potential pitfalls in this heightened attention to resistance however, as it has often lapsed into essentialist notions of timeless indigenous agricultural practices, and unproblematic local allegiances to common property and conservation. An exclusive emphasis on resistance also offers little understanding of widespread smallholder participation in plantation production across the region. A useful method of approaching the complexity of local responses to plantation development is through the history of legal and informal resource tenure, within an analysis of rural political‐economic restructuring. Drawing on research in Thailand and Sarawak, I suggest that a more nuanced appreciation of both the structural constraints and deployments of agency which characterise the enrolment of rural people into plantation commodity networks, opens up new spaces for analysis and political action, which supports a geographically embedded view of relations of power, rural livelihoods and environmental politics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".