LAND DILEMMAS IN BRAZIL: THE HISTORY OF LAND PRIVATIZATION IN BRAZIL
Bibliographic record
Abstract
This article aims to present, from a historical perspective, the main legal instruments on access to land, based on authors who have studied the agrarian issue in Brazil, namely: João Pedro Stédile (2011), José de Souza Martins (1980), Marcia Maria Mendes Motta (1998), Maria Ligia Osório Silva (1996, 1997), Ruy Cirne Lima (1954), among others. It also aims to demonstrate how legislation has advanced in the process of recognizing the socio-environmental function of property while it brought ambiguities that have led to regression in terms of a fairer and more equitable distribution of land, essential to human needs, such as housing and food. The materials used to prepare the article came from primary and secondary sources collected through bibliographic and documentary research.KEYWORDS: property of land, socio-environmental function of property, Brazilian legislation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".