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Record W2134734776 · doi:10.3390/bs3010133

The Search for Cognitive Terminology: An Analysis of Comparative Psychology Journal Titles

2013· article· en· W2134734776 on OpenAlexaff
Cynthia Whissell, Charles I. Abramson, Kelsey R Barber

Bibliographic record

VenueBehavioral Sciences · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTerminologyComparative psychologyOperationalizationPsychologyCognitionCognitive psychologyBasic sciencePsychological researchExperimental psychologyCognitive linguisticsCognitive scienceLinguisticsSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

This research examines the employment of cognitive or mentalist words in the titles of articles from three comparative psychology journals (Journal of Comparative Psychology, International Journal of Comparative Psychology, Journal of Experimental Psychology: Animal Behavior Processes; 8,572 titles, >100,000 words). The Dictionary of Affect in Language, coupled with a word search of titles, was employed to demonstrate cognitive creep. The use of cognitive terminology increased over time (1940-2010) and the increase was especially notable in comparison to the use of behavioral words, highlighting a progressively cognitivist approach to comparative research. Problems associated with the use of cognitive terminology in this domain include a lack of operationalization and a lack of portability. There were stylistic differences among journals including an increased use of words rated as pleasant and concrete across years for Journal of Comparative Psychology, and a greater use of emotionally unpleasant and concrete words in Journal of Experimental Psychology: Animal Behavior Processes.

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)0.008
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0740.064
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.330
GPT teacher head0.465
Teacher spread0.135 · 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.

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

Citations13
Published2013
Admission routes1
Has abstractyes

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