Decriminalizing Drugs: A Comparative Study of Oregon in an International Context
Bibliographic record
Abstract
Oregon made history in 2020 when voters joined together to approve ballot measure 110, the Drug Addiction Treatment and Recovery Act, which decriminalized personal amounts of all illicit substances. This was done in a bid to begin treating the ongoing drug crisis as a public health issue as opposed to a criminal justice issue. While Oregon may be the first in the nation to make such a move, they are not the first government to experiment with decriminalizing 'hard drugs.' Some argue that Oregon’s model was based on Portugal's decriminalization effort and point to Portugal's success as a potential outcome for Oregon's policy shift. However, it seems irresponsible to expect the same results when Oregon’s policy is not very closely modeled after Portugal’s. This thesis seeks to present a comparison of Oregon’s decriminalization policy amid an international discussion of the decriminalization policies in British Columbia, Canada, where government officials have just passed a temporary experimental exemption of criminal punishment for certain substances, and Portugal, where decriminalization has been regarded a great success. While not intended to present any location's policy as ‘better’ than another, this thesis was crafted to present information on each location's decriminalization policy before providing commentary on how each policy compares and contrasts with the others. Through comparison, questions on whether Oregon's decriminalization policy can claim to be modeled after Portugal's or expect the same success arise. The author hopes to contribute to ongoing discussions regarding decriminalization and question the content and validity of current policy comparisons with future policy modeling in mind.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".