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Record W3165933122 · doi:10.1111/jpr.12361

History of Psychological Testing from the Perspective of Test Developers in Japan<sup>1</sup>

2021· article· en· W3165933122 on OpenAlexaff
Tomoko Suzuki

Bibliographic record

VenueJapanese Psychological Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsIntertek (Canada)
FundersJapan Society for the Promotion of Science
KeywordsTest (biology)Perspective (graphical)PsychologyPsychological testingScale (ratio)Wechsler Adult Intelligence ScaleApplied psychologyIntelligence quotientSocial psychologyDevelopmental psychologyCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this study, the history of psychological testing in Japan is described using the oral history method. Seven test developers discussed the development of Japanese versions of the Wechsler Intelligence Scale, Binet‐Simon Intelligence Scale, and Kaufman Assessment Battery for Children. Three conclusions were identified. First, the motivation for developing the tests shifted from one of personal aspiration to a wider responsibility of specialists' desiring to make social contributions. Second, the test developers shifted from working in small, familiar, collaborative groups to working in groups of specialists conducting well‐organized projects. Third, the development of tests has variously been led by researchers, publishers, researcher‐and‐publisher collaborations, or institutes. In addition, this study identifies contemporary challenges for developing psychological tests, specifically due to insufficient numbers of participants and test developers.

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0040.003
Scholarly communication0.0020.003
Open science0.0010.003
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 head0.460
GPT teacher head0.550
Teacher spread0.090 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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