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Record W1907672044 · doi:10.1002/meet.2014.14505101066

Influence of need for cognition and need for cognitive closure on three information behavior orientations

2014· article· en· W1907672044 on OpenAlexaff
Alexandre Fortier, Jacquelyn Burkell

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsCognitionPsychologyNeed for cognitionIndependence (probability theory)PreferenceClosure (psychology)Orientation (vector space)SimplicityCognitive styleScale (ratio)Cognitive psychologySocial psychologyDevelopmental psychologyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Need for Cognition and Need for Cognitive Closure are two stable traits that can enlighten the understanding of inter‐individual variations in information behavior. Following a qualitative phase, an information behavior scale was developed using items related to the ways in which information is needed, sought, used and shared. This scale was tested with 122 undergraduate students. Results of a factor analysis indicated three different and non‐mutually exclusive aspects of information behavior: orientation to rule following, preference for familiarity and simplicity, and desire for intellectual independence. Analyses of variances for each factor, using scores for Need for Cognition and Need for Cognitive Closure as independent variables, indicated that these two traits produce significant effects on information behavior. A significant main effect of Need for Cognition was observed for the orientation to rule following and the desire for intellectual independence. A significant main effect of Need for Cognition was observed for the preference for familiarity and simplicity. Lastly, a significant interaction effect between Need for Cognition and Need for Cognitive Closure was also observed for the desire for intellectual independence.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.004
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 head0.015
GPT teacher head0.308
Teacher spread0.293 · 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 designQualitative
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

Citations23
Published2014
Admission routes1
Has abstractyes

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