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Record W2887569066 · doi:10.1177/0954406218792583

Need for Closure and individual tendency for design fixation and functional fixedness

2018· article· en· W2887569066 on OpenAlexafffund
Jason Ho, L. H. Shu

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClosure (psychology)PredictabilityFixation (population genetics)Scale (ratio)AmbiguitySocial psychologyPsychologyComputer scienceMathematicsStatisticsSociologyPolitical scienceDemography

Abstract

fetched live from OpenAlex

Past work explored the use of Kruglanski’s Need for Closure scale to separately predict individual tendency for design fixation and functional fixedness. The Need for Closure scale is a social-psychological individual difference variable that has five subscales: (1) order, (2) predictability, (3) decisiveness, (4) ambiguity, and (5) closed-mindedness. In a past study on design fixation, participants were asked to develop concepts for which an example solution was provided, and correlations were found between participants’ score on the Need for Closure scale and the degree of fixation in their concepts. In a separate study on functional fixedness, participants were asked to identify alternative uses for everyday objects, and correlations were found between measures of functional fixedness and components of Need for Closure. The current work explored whether individual tendency for design fixation and functional fixedness could be related, combining similar methods used in past work. While no significant relationship was found between measures for design fixation and functional fixedness, significant results are related to, and further elucidate past work.

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 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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.039
GPT teacher head0.256
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicDesign Education and PracticeFrench-language works237,207