Rural Commons and the Experience of the Movimento Sem Terra in Brazil
Bibliographic record
Abstract
The objective of this paper is to analyse the experience of the Movimento Sem Terra in Brazil, with the analysis of a single case study, in order to highlight how it resonates with the theory of commons and rural commons, and how such practices can advance sustainable development and inter-generational equity. This topic is particularly interesting given the growing interest for commoning practices and their positive approach toward environmental issues. The research will be conducted by using a qualitative method based on a single case study, which was chosen for its relevance in the local context and for the possibility of accessing primary information. Results indicate that the approach of the MST, and of participants in the Dorcelina Folador settlement, is quite similar to other commoning practices and strongly focused on caring for land and making it flourish, rather than ensuring private ownership for profit-making purposes. In fact, settlers state that they do not want to become land owners, as they see land as something sacred and belonging to the community as a whole, and their objective is to ensure that it is protected. Moreover, the practices carried out by Movimento Sem Terra usually lead to improved conditions in terms of agricultural results, biodiversity and environmental protection, indicating the movement’s strong drive toward sustainability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".