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Guidelines for reporting action simulation studies (GRASS): Proposals to improve reporting of research in motor imagery and action observation

2023· review· en· W4388591410 on OpenAlexaff
Marcos Moreno‐Verdú, Gautier Hamoline, Elise E Van Caenegem, Baptiste M Waltzing, Sébastien Forest, Ashika Chembila Valappil, Adam Khan, Samantha Chye, Maaike Esselaar, Mark J. Campbell, Craig McAllister, Sarah N. Kraeutner, Ellen Poliakoff, Cornelia Frank, Daniel Eaves, Caroline Wakefield, Shaun G. Boe, Paul S. Holmes, Adam Bruton, Stefan Vogt, David J. Wright, Robert M. Hardwick

Bibliographic record

VenueNeuropsychologia · 2023
Typereview
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsDalhousie UniversityUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAction (physics)ChecklistPsychologyKey (lock)Action researchComputer scienceApplied psychologyCognitive psychologyComputer security

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)0.247
metaresearch head score (Gemma)0.448
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2470.448
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0110.013
Bibliometrics0.0270.021
Science and technology studies0.0040.012
Scholarly communication0.0110.008
Open science0.0230.011
Research integrity0.0190.017
Insufficient payload (model declined to judge)0.0070.009

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 head0.874
GPT teacher head0.675
Teacher spread0.199 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations40
Published2023
Admission routes1
Has abstractno

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