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Record W4361305706 · doi:10.1080/15248372.2023.2188946

The Future of Research on Executive Function and Its Development: An Introduction to the Special Issue

2023· article· en· W4361305706 on OpenAlex
Sabine Doebel, Ulrich Müller

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 Cognition and Development · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeneralityFunction (biology)PsychologyWarrantContext (archaeology)CognitionSet (abstract data type)Executive summaryField (mathematics)Empirical researchCognitive psychologyCognitive scienceEpistemologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Over the last several decades, research on executive function in children has flourished, producing a wealth of empirical findings. These findings have raised many theoretical and methodological questions that warrant attention and are addressed in this special issue. This introduction to the special issue reviews some of the recent history of the field before introducing the seven target articles. We introduce these articles in the context of current theoretical and methodological issues: domain generality versus domain specificity of executive function, ecological and cultural validity of executive function measures, executive function training and transfer, and the nature of relations between executive function and achievement and other outcomes. This diverse set of articles collectively provides many fresh, testable ideas that promise to advance the field and usher in the next wave of theory-guided executive function research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.356
Teacher spread0.308 · 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