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Record W3103194122 · doi:10.1016/j.drugpo.2020.103013

Fentanyl analogs on the Swedish webforum flashback: Interest and impact of scheduling

2020· article· en· W3103194122 on OpenAlexaff
Kim Moeller, Bengt Svensson, Rasmus Munksgaard

Bibliographic record

VenueInternational Journal of Drug Policy · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFentanylScheduling (production processes)MedicinePsychologyAnesthesiaEngineeringOperations management

Abstract

fetched live from OpenAlex

BACKGROUND: Sweden regulates new psychoactive substances, including fentanyl analogs, individually. This reactive scheduling procedure enabled the existence of a recreational market for unscheduled fentanyl analogs sold from surface webshops. We measure the interest in 24 named fentanyl analogs and the impact of scheduling. METHODS: We scraped posts in threads on named fentanyl analogs from the Swedish internet forum Flashback.org, 2012-2019. The sample consists of 24 threads with a total of 8761 posts. We construct five measures of interest based on duration of threads, number of posts, and number of distinct posters, and fit a non-seasonal ARMA model to test if there was a change in mean activity after scheduling. RESULTS: Across the five measures, there was most interest in acryl fentanyl, butyr fentanyl, and acetyl fentanyl. The number of daily posts was significantly reduced in nine out of 13 threads after scheduling. CONCLUSION: The scheduling of fentanyl analogs impacted interest on Flashback.org. The biggest effect sizes were from the narcotics scheduling of 2-Me-MAF, acryl, and acetyl fentanyl, while furanyl fentanyl saw the biggest reduction after health scheduling. The reductions were bigger for narcotics scheduling compared to health scheduling.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.780

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.453
Teacher spread0.350 · 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 teacher head, 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

Citations6
Published2020
Admission routes1
Has abstractyes

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