Land grabbing and global capitalist accumulation: key features in Latin America
Bibliographic record
Abstract
We introduce this special issue by explaining seven characteristics of land grabbing in Latin America. These features are not unique to the region. By highlighting them – arguing, for instance, that a key aspect in Latin America is intra-regional land grabbing driven by (trans)Latina companies – we hope to inspire new cross-regional comparisons to understand the dynamics of “global” land grabbing. Our focus on Latin America challenges some problematic generalisations in the literature, for instance, that land grabs occur mainly in fragile states. We interrogate the relationship between land grabbing and the “foreignisation” narrative, and the need to revisit the broader question of land concentration. Thus we build upon the literature locating land grabs and the land question within the political economy of global capitalism. Cette introduction au numéro spécial présente sept caractéristiques de l'accaparement des terres en Amérique latine, caractéristiques qui ne sont pas propres à la région. En les mettant en lumière, notamment en soutenant qu'en Amérique latine le phénomène, impulsé par des compagnies latino-américaine, est avant tout intra-régional, nous espérons susciter de nouvelles comparaisons inter-régionales afin de mieux comprendre les dynamiques mondiales de l'accaparement de terres. L'accent mis sur Amérique latine permet de remettre en question des généralisations auxquelles s'adonnent de nombreux écrits, comme par exemple que l'accaparement se produit principalement dans les états fragiles. Nous examinons les liens établis entre l'accaparement de terres et la main mise étrangère pour montrer la nécessité de réévaluer la question élargie de la concentration de terres. Les articles du numéro situent l'accaparement des terres dans le cadre de l’économie politique du capitalisme mondial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".