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Record W4387380009 · doi:10.1556/2006.2023.00052

The landscape of open science in behavioral addiction research: Current practices and future directions

2023· review· en· W4387380009 on OpenAlex
Charlotte Eben, Beáta Bőthe, Damien Brevers, Luke Clark, Joshua B. Grubbs, Robert Heirene, Anja Kräplin, Karol Lewczuk, Lucas Palmer, José C. Perales, Jan Peters, Ruth J. van Holst, Joël Billieux

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Behavioral Addictions · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of British ColumbiaUniversité de Montréal
Fundersnot available
KeywordsAddictionPsychologyOpen scienceCurrent (fluid)Neuroscience

Abstract

fetched live from OpenAlex

Open science refers to a set of practices that aim to make scientific research more transparent, accessible, and reproducible, including pre-registration of study protocols, sharing of data and materials, the use of transparent research methods, and open access publishing. In this commentary, we describe and evaluate the current state of open science practices in behavioral addiction research. We highlight the specific value of open science practices for the field; discuss recent field-specific meta-scientific reviews that show the adoption of such practices remains in its infancy; address the challenges to engaging with open science; and make recommendations for how researchers, journals, and scientific institutions can work to overcome these challenges and promote high-quality, transparently reported behavioral addiction research. By collaboratively promoting open science practices, the field can create a more sustainable and productive research environment that benefits both the scientific community and society as a whole.

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)0.170
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1700.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0040.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0060.001
Research integrity0.0000.002
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 head0.917
GPT teacher head0.694
Teacher spread0.223 · 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