Executive Summary Fiscal Policy in the Regulation of Adult-Use Cannabis in Colombia
Bibliographic record
Abstract
The debate about regulating cannabis for adult use is on the public agenda. In our view, the best policy on marijuana that a State can develop is the regulation of its cultivation, manufacture and use, focused on reducing marijuana’s comparative impact in illegal economies and drug trafficking networks; protecting public health; promoting rural development in prioritized areas; and promoting reparation measures financed with the resources arising from regulation. Drugs are not the devil, but nor are they child’s play. A drug policy that would be respectful of human rights and safeguard public health must lie at an intermediate point between full liberalization and the prohibition currently in place. In this document, based on a comparative analysis of the regulations issued in Uruguay, Canada and the United States and by applying the Principles and Guidelines for Human Rights in Fiscal Policy, we argue for the importance of a fiscal framework based on collecting taxes in the cannabis market and focused on mobilizing the maximum amount of available resources to finance the goals of reducing the illegal market, preserving public health and assisting the populations affected by drug policy, as set forth in the cannabis regulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".