Bibliographic record
Abstract
Peasants and rural landless workers have been at the forefront of recent social movements in Latin America. In fact, we are witnessing a resurgence of ruralbased social movements precisely when some analysts are predicting the “death of the peasantry” because of the advance of capitalism. After providing some conceptual and historical background on peasants, we move on in this chapter to consider first the case of Brazil, where the MST (Movimento dos trabalhadores rurais Sem Terra: Landless Workers’ Movement) has become one of the most significant rural movements to date. This is followed by a case study of Colombia, where the peasant-based FARC (Fuerzas Armadas Revolucionarias de Colombia: Revolutionary Armed Forces of Colombia) once controlled a territory the size of Switzerland in “independent republics”, and is thus a unique case. The next case study is of Chile, where we have seen a more “classic” pattern of capitalist modernization of agriculture and large-scale agrarian reforms. Finally, we turn to the transnational domain, where Vía Campesina, which originated in Latin America, has for the first time shown the potential of the peasantry as a transnational counter-globalization force. This cluster of cases, taken together, shows the great capacity for resistance and innovation of Latin America's rural dwellers. In Latin America The second major historical social movement in Latin America after the workers’ movement is that of the peasants and landless rural workers. From the Mexican Revolution in 1910 through to the Cuban Revolution in 1959 and the long-running insurgency in Colombia, peasants have been at the forefront of social and political transformation in Latin America. Old and new forms of capitalist exploitation combined in rural areas and so have the forms of rural dweller resistance. Thus, in Colombia, a long-standing tradition of rural organization and revolt going back to the 1950s was combined with communist insurgency models to turn the “independent republics” into instances of dual power. In a different way, in Brazil, we saw in the early 1960s a combination of peasant leagues organizing smallholders with a national confederation of rural workers, CONTAG (Confederação dos Trabalhadores na Agricultura: Agricultural Workers’ Confederation), organizing rural workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.229 | 0.059 |
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".