Dataset: Repeated and longitudinal measures database from 1994 to 2018 to assess Chile's substance use control policies for the 2000–2010 decade
Bibliographic record
Abstract
We developed a database to assess Chile's substance use control policies implemented in the 2000-10 decade. The database includes the measurement of consumption of substances such as alcohol, tobacco, and drugs (cannabis, cocaine, and “pasta base” (crack)), individual, relationships, and environmental factors related to substance use, and variables that measure the implementation of laws regulating its use. For the construction of the database, we used information from three sources: i) the biannual National Survey of Drug Consumption for the general population of the National Service of Prevention and Rehabilitation for Drug and alcohol consumption (SENDA) from the Chilean government, ii) the cases filed in local police courts by group of offenses from Chile's Ministry of Justice reports, and iii) the regional imprisoned population from Chile's Correctional Services reports. In the case of the first data source, a data curation process was established to construct this unique database from 1994 to 2018, identifying variables measured systematically over time, standardizing variables' operationalization, and adjusting responses to prespecified flows in each year. On the other hand, substance use control laws enacted in 2004 (alcohol), 2005 (drugs), and 2006 (tobacco) were operationalized as categorical and continuous variables as indicators of its implementation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".