CRACK COCAINE USE SCENE IN THE CAPITAL OF THE STATE OF SANTA CATARINA/BRAZIL: THE (IN)VISIBILITY OF USERS
Bibliographic record
Abstract
ABSTRACT Objective: to describe the characteristics of the crack cocaine use scene, its surroundings and consequences. Method: this study was based on the Time-location Sampling methodology. Between January and June 2011, 41 crack use scenes were mapped in Florianópolis (Brazil). After randomly selecting the scenes to be observed, the days and shifts for in-depth observation were selected by lottery, for a total of 98 scenes/shifts, this atep was performed between December 2011 and March 2012. The observations were recorded in a field diary, and were examined using content analysis and discussed based on the Brazilian and international literature on the topic. Results: the results show that crack cocaine use scenes were more concentrated in the central regions of Florianópolis. Policing was very ostensive in the communities surrounding these areas, which are strongly marked by drug trafficking. Healthcare, prevention and authority actions were incipient in the locations of substance use, which shows the invisibility of crack users in society. Conclusions: more investments are needed so that public policies work to help drug users access social and healthcare services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".