Uruguay Cannabis Market Dataset: CBDT Framework Natural Experiment Validation (2017-2025)
Bibliographic record
Abstract
This dataset documents Uruguay's cannabis market from 2017-2025, capturing a natural experiment where legal cannabis quality (THC potency) systematically increased from 3% to 20% while price remained constant at approximately $1.40/gram. Key Finding: When Uruguay increased legal cannabis from 9% to 15% THC in December 2022, sales increased 84% (1,774 to 3,258 kg) in one year while price remained constant, isolating quality as the primary driver of legal market adoption. The data validates the Consumer-Driven Black Market Displacement (CBDT) Framework across four distinct quality eras. Statistical validation shows the framework explains 92.9% of variance in adoption rates (R² = 0.9288, p = 0.036) with mean absolute error of 2.0 percentage points. Dataset Contents: 6 CSV files with complete market data (registrations, sales, THC levels, framework scores) Era-level analysis (4 periods: 2017, 2018-2021, 2022-2023, 2024-2025) Time-series data (14 observations across 8 years) Statistical validation results (regression analysis, confidence intervals) Complete source documentation (11 independent sources cross-verified) Python replication code for all analyses Related Publications: The Silent Majority 420 (2025). Consumer-Driven Black Market Displacement (CBDT) Framework: A Behavioral-Utility Heuristic for Illicit-to-Legal Market Transition. Zenodo. https://doi.org/10.5281/zenodo.17593077 The Silent Majority 420 (2025). CBDT Framework Canadian Validation: Cross-National Evidence of Cultural Homogeneity Effects. Zenodo. https://doi.org/10.5281/zenodo.17611991 Primary Data Sources: IRCCA (Instituto de Regulación y Control del Cannabis, Uruguay), European Union Drugs Agency (2018), London School of Economics Journal of Illicit Economies and Development (2025). Replication: All analyses fully reproducible using provided CSV files and Python scripts. Complete methodology documented in DATASET_README.md included in files.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.031 | 0.022 |
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".