Commons grabbing and agribusiness: Violence, resistance and social mobilization
Bibliographic record
Abstract
The recent phenomenon of large-scale land acquisitions (LSLAs) is associated with what has been described as a global agrarian transition. New forms of land exploitation and concentration have led to profound socio-environmental transformations of rural production systems in Latin America, South-East Asia and Sub Saharan Africa. Scholars have pointed out that the expansion of transnational land investments is often associated with detrimental social outcomes, has negative environmental impacts and can represent a potential impediment to the achievement of many SDGs. In this paper, our primary concern is on the mounting evidence that LSLAs preferentially target the commons, in the process altering long-standing customary resource governance systems. While it has been shown that in many instances of commons grabbing associated with LSLAs, different types of social conflict emerge, it is less clear what forms of social mobilization and organized collective re-actions are taking place to defend the commons and contest such processes of dispossession and enclosure. The main aim of this contribution is to fill this gap by synthesizing and describing the different typologies of social mobilization and collective re-actions that emerge as a result of commons grabbing associated with the transnational expansion of the agribusiness frontier. In order to do this our research synthesizes information from the Environmental Justice Atlas (EJAtlas) shedding light on some of the key characteristics associated with the different forms and dynamics of social mobilization that are organized in reaction to agribusiness-related commons grabbing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".