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Record W1983437128 · doi:10.1177/0165025407077765

State space grids: Analyzing dynamics across development

2007· article· en· W1983437128 on OpenAlex

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

VenueInternational Journal of Behavioral Development · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocioemotional selectivity theoryState spaceGridSpace (punctuation)Computer scienceStability (learning theory)Dynamics (music)Data sciencePsychologyCognitive psychologyManagement scienceDevelopmental psychologyMachine learningMathematics

Abstract

fetched live from OpenAlex

Developmentalists are generally interested in systems perspectives and this is reflected in the theoretical models of the past decade. However, the methodological tools to test these models are either nonexistent or difficult for many researchers to use. This article reviews the state space grid (SSG) method for analyzing synchronized event sequences based on dynamic systems (DS) principles. Following a review of these DS concepts and the basics of the SSG method, several studies are reviewed. Greater emphasis and detail are provided for three longitudinal studies that relate real-time socioemotional dynamics to processes of developmental change and stability. The concluding sections provide guidelines for researchers interested in using the SSG method and some suggestions for future SSG studies.

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)0.008
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.135
GPT teacher head0.461
Teacher spread0.326 · 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