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Record W2211460512 · doi:10.1002/sres.2388

Situating a Measure of Systems Thinking in a Landscape of Psychological Constructs

2015· article· en· W2211460512 on OpenAlexaff
Paul H. Thibodeau, Cynthia McPherson Frantz, Mirella L. Stroink

Bibliographic record

VenueSystems Research and Behavioral Science · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsLakehead University
Fundersnot available
KeywordsSystems thinkingMindsetScholarshipEpistemologyCritical systems thinkingRelation (database)PsychologyVertical thinkingSociologyUnderpinningFoundation (evidence)Measure (data warehouse)Perspective (graphical)Social psychologyCritical thinkingConvergent thinkingComputer scienceCreativity

Abstract

fetched live from OpenAlex

Many of the greatest challenges in society have emerged as a result of humans acting within complex systems without fully understanding how they work. To address this problem, scholars from diverse fields have appealed to systems thinking. To date, a psychological perspective has been conspicuously absent from scholarship on this topic—a gap that the present paper seeks to fill by situating an individual difference measure of systems thinking in relation to well-studied constructs (e.g. holistic and relational thinking) and decision-making tasks in the psychological literature. Results indicate that the measure of systems thinking captures peoples' tendency to represent and reason about complex systems. The paper helps to validate a novel measure of an individual's tendency to engage in systems thinking and to provide a conceptual foundation for the thinking about the psychological underpinning of a systems thinking mindset. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.534
GPT teacher head0.541
Teacher spread0.006 · 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 designObservational
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

Citations33
Published2015
Admission routes1
Has abstractyes

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