Should All Illicit Drugs be Legalized?
Bibliographic record
Abstract
Background: In delving into the intricacies and nuances surrounding the debate of whether or not all illicit drugs should be legalized, the goal was to compare and contrast the research findings of various different economic, medical and sociological studies to determine if the prospective benefits of legalization outweigh the potential cons—with respect to the current systems in North America (Canada and the United States). Methods: By looking at different data that was collected and interpreted by economists and sociologists as well as incarceration rates and statistics provided by the U.S. Bureau of Justice Statistics database, we can assess the societal costs of drug criminalization and pit this against the potential costs that would be incurred if all drugs were to be legalized. To understand these potential costs we look at the leading causes of drug overdose and addiction in society using collected data from drug overdose toxicology reports as well and crowdfunded survey data and government records. In assessing the different risks posed by certain drugs, we have also taken into consideration the empirical lab studies of medical practitioners and neuroscientists working in the fields of addiction and toxicology. Results: The research indicates that while insufficient data exists to completely dispel the potential risks associated with the legalization, the evidence to suggest that the criminalization is an effective deterrent from abusing drugs is also insubstantial. The economic merits that would come from the legalization ofdrugs, through tax and labour income as well as through the reduction of criminal justice costs, are demonstrably significant and feasible to implement. Conclusions/Interpretation: When compared and contrasted, the research evidenced in these studies suggests that the merits of legalization outweigh the risks associated with doing so.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".