“Tihik quando bebe Kaxmuk não tem pai, nem mãe, nem irmão”: Percepções sociais das consequências do uso da cachaça no povo indígena Maxakali/MG/Brasil
Bibliographic record
Abstract
This study explores one of the most interesting and least studied issues in Brazil: the consequences of complex and contradictory experiences by replacing the traditional drinks by cachaça, introduced through interethnic contact. Given the rarity of the study of Maxakali alcohol consumption in research, this study aims to understand, from the native's point of view, the negative aftereffect of alcohol consumption. Although anthropological studies emphasize functions of traditional and contemporary drinking as social "lubricants", social perceptions of the Maxakali highlight the problems of cachaça bought through interethnic contact. Symbols and meanings of these consequences were interpreted through their daily life histories, recorded by 21 leaders in focus group. Through the interethnic contact, some adaptations have occurred in the Maxakali alcohol use, with negative consequences for those who drink, their families, their villages and their community. In the world-of-life, these changes these changes can be seen through accidents, insults, marital disharmony, neglects, violent behavior, illness and death. This study's findings highlight the importance of producing comprehensive and in-depth knowledge in search of to identify vulnerable groups and to develop participatory solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".