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Record W2404524356 · doi:10.1080/17440572.2016.1179632

How MDMA flows across the USA: evidence from price data

2016· article· en· W2404524356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGlobal Crime · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMDMAMiamiEconomic geographyDrug traffickingGeographyDemographic economicsRegional scienceEconomicsSociologyPsychologyEnvironmental scienceCriminology

Abstract

fetched live from OpenAlex

This study uses wholesale prices of MDMA for 59 cities in the USA published by the National Drug Intelligence Center (NDIC) over the period of 2002–2011 to identify trafficking patterns of MDMA. Price differentials and correlations between pairs of cities are used to infer the presence of a link and the direction of flow of MDMA. The presence of inward and outward links is used to categorise each city as a ‘source’, ‘destination’, ‘transit’, or ‘weakly integrated’ city. The analysis identified low prices close to the Canadian and Mexican borders, in a number of cities such as Chicago, Miami, New York City, a trio of cities in the Carolinas, and along the West Coast. A number of these cities are linked to large numbers of other cities, indicating hub- or source-like status. The findings generate insights into the status of major US cities in the MDMA trafficking network.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.209
GPT teacher head0.467
Teacher spread0.258 · 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