Bibliographic record
Abstract
Abstract Southeast Asia has witnessed dramatic transformations in the rural sector over the past 60 years, first with the Green Revolution, and more recently with diverse multi‐scalar economic and socio‐political processes including the growth of cash crops, export processing zones, land conversions, genetically modified crops, free trade agreements, and the growing complexity of rural‐urban connections. With such a multiplicity of changes, many directly linked to globalisation, come numerous forms of resistance, as individuals and communities struggle against what they see as unjust consequences. These different resistance measures to rural transformations are the focus of this article. While critiquing the literature on different conceptualisations of resistance, from rural resistance at the micro level, through to collective action and open protest, revolutionary movements, and even regional and global transnational movements, we propose three core arguments. First, in order to capture the diversity of forms of contemporary rural resistance, one needs to use a multi‐scalar approach. Previous analytical constructs such as ‘local’ and ‘global’ are inadequate to examine and explain the forms of resistance taking place. Second, rural resistance comes in a complexity of forms, is diversifying rapidly, and is never static. Resistance measures are context contingent, shaped by different worldviews and shift according to local circumstances, the opening and closing of opportunities structures, and the endogenous peculiarities of resistance dynamics. Third, a focus on resistance to contemporary agrarian change in Southeast Asia must recognise agency. We demonstrate that there is much to be gained by taking an interactive standpoint, arguing that local, national, regional, and even global resistance has as much to do with how the actors’ themselves define their field of protest as with the specific nature of their targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".