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Record W2738572282 · doi:10.1002/sres.2475

Systems Thinkers Express an Elevated Capacity for the Allocentric Components of Cognitive and Affective Empathy

2017· article· en· W2738572282 on OpenAlexaff
Adam C. Davis, Wendy Leppanen, Kimberly P. Mularczyk, Theresia Bedard, Mirella L. Stroink

Bibliographic record

VenueSystems Research and Behavioral Science · 2017
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsLakehead University
Fundersnot available
KeywordsEmpathyPsychologyCognitionPerspective (graphical)Value (mathematics)Social psychologyPerspective-takingCognitive psychology

Abstract

fetched live from OpenAlex

Systems thinking is a cognitive style involving an implicit tendency to perceive the social‐ecological world as a multitude of interconnected complex adaptive systems. Previous research has shown that systems thinkers value and care for entities that transcend the self, such as other human beings and nature, signalling a more expansive self‐concept. Similarly, individuals high in empathy have an inclusive self‐concept, a stronger proenvironmental identity and value entities beyond themselves. The present study examined the hypothesis that systems thinking would share a positive relation with components of cognitive and affective empathy. Results from an online survey of 135 undergraduate students demonstrated that systems thinking shared significant positive correlations with perspective taking, empathic concern and fantasy. However, only perspective taking and empathic concern emerged as unique significant positive predictors of systems thinking. Results suggest that systems thinkers express an elevated capacity for the allocentric components of cognitive and affective empathy. Copyright © 2017 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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.432
GPT teacher head0.506
Teacher spread0.074 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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