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Record W1761134531 · doi:10.1111/cdev.12395

On the Meanings of Self-Regulation: Digital Humanities in Service of Conceptual Clarity

2015· article· en· W1761134531 on OpenAlexafffund
Jeremy Trevelyan Burman, Christopher D. Green, Stuart Shanker

Bibliographic record

VenueChild Development · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCLARITYPsychologyVocabularyPersonalitySelf-controlAssociation (psychology)Social psychologyControl (management)SelfCognitive psychologyCognitive scienceLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Self-regulation is of interest both to psychologists and to teachers. But what the word means is unclear. To define it precisely, two studies examined the American Psychological Association's system of controlled vocabulary-specifically, the 447 associated terms it presents-and used techniques from the Digital Humanities to identify 88 closely related concepts and six broad conceptual clusters. The resulting analyses show how similar ideas are interrelated: self-control, self-management, self-observation, learning, social behavior, and the personality constructs related to self-monitoring. A full-color network map locates these concepts and clusters relative to each other. It also highlights some of the interests of different audiences, which can be described heuristically using two axes that have been labeled abstract versus practical and self-oriented versus other-oriented.

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)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0050.058
Scholarly communication0.0140.022
Open science0.0010.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.145
GPT teacher head0.354
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
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

Citations91
Published2015
Admission routes2
Has abstractyes

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