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Record W2751994617

How Extensive is Inter-State Diversion of Recreational Marijuana?

2017· preprint· en· W2751994617 on OpenAlexaboutno aff
Benjamin Hansen, Keaton Miller, Caroline Weber

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationBlameState (computer science)PopulationGovernment (linguistics)BusinessDatabase transactionPoliticsGeographyPolitical scienceDemographyLaw
DOInot available

Abstract

fetched live from OpenAlex

Despite federal prohibition, recreational marijuana is available to 21% of the United States population. A chief concern among policy makers across multiple levels of government and political parties is inter-state diversion of marijuana from states with legal markets to others. We measure this diversion with a natural experiment. Oregon opened a recreational market on October 1, 2015 next to an existing market in Washington, which opened on July 8, 2014. Using comprehensive administrative data on the universe of Washington sales, we find Washington retailers along the Oregon border experienced a 41% decline in sales immediately following Oregon's market opening. Retailers along Washington's borders with Idaho and Canada experienced no such decline. The decline occurred equally across weekdays and weekends, and was largest among the largest transaction sizes, suggesting diversion, not drug tourism, was to blame. Our estimates suggest that 11.9% of the marijuana sold in Washington was diverted out of the state before Oregon legalized and 7.5% remains diverted today.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.042
GPT teacher head0.341
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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