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Record W1974604477 · doi:10.1080/15427609.2011.549686

Developmental Systems Science: Exploring the Application of Systems Science Methods to Developmental Science Questions

2011· article· en· W1974604477 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

VenueResearch in Human Development · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDevelopmental ScienceSystems sciencePerspective (graphical)Cognitive scienceComputer sciencePsychologyEngineering ethicsData scienceManagement scienceDevelopmental psychologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Developmental science theorists fully acknowledge the wide array of complex interactions among biology, behavior, and environment that together give rise to development. However, despite this conceptual understanding of development as a system, developmental science has not fully applied analytic methods commensurate with this systems perspective. This article provides a brief introduction to systems science, an approach to problem solving that involves the use of methods especially equipped to handle complex relationships and their evolution over time. In addition, a rationale is provided for why and how these methods can serve the needs of the developmental science research community.

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)0.132
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1320.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.026
Science and technology studies0.0050.005
Scholarly communication0.0020.002
Open science0.0080.004
Research integrity0.0000.001
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 head0.721
GPT teacher head0.580
Teacher spread0.141 · 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