Bibliographic record
Abstract
This article presents extensive data on land purchases and leases in Uruguay in 2000–2010, and complements it with information from interviews with leaders and officials. The evidence points to an acute process of land concentration and “foreignisation” in Uruguay. This process, however, meets only a few of the criteria for “land grab”, in the narrow definition of the term, suggesting that the definition should be widened to include what is taking place in Uruguay. Neither the political system nor broader society has paid sufficient attention to these processes. This is perhaps because the recent surge in economic growth and social wellbeing mask their long-term consequences. Cet article présente un important ensemble de données pour la période de 2000 à 2010 sur l'acquisition de par achat ou par bail foncier terres en Uruguay. Ces données sont complétées par des informations issues d'entretiens auprès des d'acteurs des partis politiques, des syndicates agricoles et des associations industrielles. L'analyse montre qu'un processus de concentration foncière et de main mise étrangère sur les terres est en cours dans le pays. Toutefois, ce processus correspond mal à certains critères qui définissent habituellement l'accaparement des terres. Cette observation nous invite à réviser cette définition pour tenir compte de ce qui se passe en Uruguay, soit qu'une part de l'accaparement vise le marché de la pulpe et qu'il ne repose pas sur des investissements provenant de gouvernements étrangers. Ni les autorités politiques ni plus largement la société n'y ont accordé suffisamment d'attention, ce qui est probablement dû à la poussée récente de la croissance économique et de l'amélioration du bien-être social qui occultent les conséquences à long terme du processus de concentration foncière et de main mise étrangère.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".