Moral Networks: A Sociological Study on Illicit Self-Cultivation of Cannabis for Psychoactive use in Brazil
Bibliographic record
Abstract
The use of psychoactive drugs is a social practice commonly observed in all societies. This article aims to present the moral grammar of actors who grow marijuana for their own use in Brazil. This study employed a qualitative methodology based on direct observation and semi-structured interviews. Regarding research ethics, all institutional principles were considered, like obtaining informed consent and guaranteeing the privacy of participants and the confidentiality of information. We found that these actors establish a sui generis morality through their practices. From this perspective, it can be conjectured that this network of actors who grow their own marijuana configures a specific moral grammar through the language devices they mobilize in response to the judgments, criticisms and moral accusations they face, whether formal or informal. Moreover, through the interviews, it was possible to verify how the relations of mutual assistance in this moral network of actors who grow their own marijuana are shaped by the actions, interactions, associations and moral aggregations among them. In this way, the relations of reciprocity and cooperation among these moral actors configure a kind of solidarity specific to this network. Therefore, the home cultivation of marijuana is analyzed as a legitimate moral feeling of liberation in relation to the formal and informal repressions faced by these actors.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".