Land control dynamics and social-ecological transformations in upland Philippines
Bibliographic record
Abstract
Recent scholarship on land grabs has begun exploring the complexity of local dynamics of land control, with emphasis on the concepts of access and exclusion and the social processes that influence these two. In this paper, I emphasize that a nuanced examination of broader social-ecological transformations would enrich our understanding of land control and exclusion. Drawing from field research in the Philippine province of Palawan, this paper examines how the combined effects of the practices of conservation enclosures, the uneven land accumulation brought about by oil palm expansion, and the use of legitimizing upland discourses all contribute to social-ecological transformations in swidden and the exclusion of indigenous smallholders from benefiting from integral forms of swidden agriculture. These practices of land control and the associated social-ecological transformations are not just interconnected, but also characterized by feedback mechanisms. The more smallholders decide to alter (or abandon) swidden practices, participate in oil palm contract farming, and/or sell their land to oil palm growers, the greater the tendency for land to accumulate among migrant settlers and absentee landowners. This, in turn, may lead to further reduction in the availability of fallow land and exclusion of more indigenous smallholders over time.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".