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Record W4416995107 · doi:10.1037/pha0000809

Delay discounting violations vary by adolescent sociodemographics: Excluding nonsystematic data may bias conclusions.

2025· article· en· W4416995107 on OpenAlexaff
Brett W. Gelino, Julia W. Felton, I‐Tzu Hung, Justin C. Strickland, Geoffrey Kahn, Nathaniel Thomas, Joshua L. Gowin, Matthew E. Sloan, Abraham A. Palmer, Sandra Sanchez‐Roige, Marcos Sanches, Sarah W. Yip, Brion S. Maher, Jill A. Rabinowitz

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersNational Institutes of Health
KeywordsDiscountingReceiptDelay discountingAddictionCognitionValuation (finance)Psychological scienceCognitive bias

Abstract

fetched live from OpenAlex

= 11,307), we examined whether nonsystematic responding covaried with demographic, cognitive/behavioral, and environmental characteristics. Nearly half of participants exhibited at least one nonsystematic responding violation, with greater likelihood among youth from low-income households, low-resource neighborhoods, and racially minoritized backgrounds. Nonsystematic responding was also associated with lower abstract reasoning and higher positive urgency. Violations disproportionately occurred at the earliest presented task delays, suggesting a possible learning effect. These findings raise concerns that data exclusion criteria may bias behavioral samples and alter conclusions in translational research domains such as addiction science, behavioral pharmacology, and public health. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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)0.012
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.294
GPT teacher head0.562
Teacher spread0.268 · 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 designObservational
DomainMethods
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

Citations1
Published2025
Admission routes1
Has abstractyes

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