Land concentration and foreign land ownership in Argentina in the context of global land grabbing
Bibliographic record
Abstract
This analysis of the role and dynamic of land concentration and foreign land ownership in Argentina describes the scale of concentration in the agricultural sector, focusing on large domestic firms, foreign companies and capital, and the possible presence of land grabbing. The examination of large units shows the different forms that access to land may take, the importance of factors other than the size of farm properties and the diversity found among these companies. Our examination of the various characteristics of land grabbing indicates what effects these processes may have both on family farms and on the global food supply. Cette étude de la dynamique de concentration des terres et de la propriété foncière étrangère en Argentine décrit l'ampleur de ces tendances dans le secteur agricole en mettant l'accent sur le rôle des grandes firmes nationales, sur celui des firmes et des capitaux étrangers, ainsi que sur la présence possible d'un phénomène d'accaparement des terres. L'analyse des grands holdings met en lumière les formes variées de l'accès à la terre, l'importance de facteurs autres que la taille des exploitations agricoles ainsi que la diversité des sociétés impliquées dans les changements agricoles en cours. Après examen de la diversité des formes d'accaparement des terres, l'article s'interroge sur les conséquences des dynamiques d'accaparement pour les exploitations familiales et pour l'approvisionnement mondial en produits alimentaires.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".