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Record W4410511830 · doi:10.1556/2006.2025.00033

A clarion call to the addiction science community: It's time to resist the anti-scientific policies of the US Trump administration

2025· editorial· en· W4410511830 on OpenAlexaff
Thomas F. Babor, Bryon Adinoff, Luke Clark, David Crockford, Zsolt Demetrovics, Paul Dietze, Jean‐Sébastien Fallu, Sally Gainsbury, Gail Gilchrist, David A. Gorelick, Kathryn Graham, Jason Grebely, Derek Heim, Matilda Hellman, Anne‐Marie Laslett, Caravella McCuistian, Michal Miovský, Neo K. Morojele, Jacek Moskalewicz, Isidore Obot, Richard Pates, Robin Room, Marta Rychert, Aysel Sultan, Carla Treloar, Nigel E. Turner, Samantha Wells, Emily C. Williams, Katie Witkiewitz

Bibliographic record

VenueJournal of Behavioral Addictions · 2025
Typeeditorial
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversité de MontréalUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsCLARIONAddictionAdministration (probate law)PsychologyInternet privacyPsychiatryPolitical scienceCognitive psychologyComputer scienceLaw

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.029
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.990
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.110
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0070.003
Science and technology studies0.0100.006
Scholarly communication0.0200.011
Open science0.0070.003
Research integrity0.0540.073
Insufficient payload (model declined to judge)0.0120.011

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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2025
Admission routes1
Has abstractno

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