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Record W2325873868 · doi:10.1037/a0039013

Searching for the structure of early American psychology: Networking Psychological Review, 1909–1923.

2015· article· en· W2325873868 on OpenAlexaff
Christopher D. Green, Ingo Feinerer, Jeremy Trevelyan Burman

Bibliographic record

VenueHistory of Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsYork University
Fundersnot available
KeywordsPeriod (music)History of psychologySpecialtyExperimental psychologyInternational psychologyPsychologySocial scienceSociologyLibrary sciencePsychoanalysisCritical psychologyPsychiatryAsian psychologyComputer science

Abstract

fetched live from OpenAlex

This study continues a previous investigation of the intellectual structure of early American psychology by presenting and analyzing 3 networks that collectively include every substantive article published in Psychological Review during the 15-year period from 1909 to 1923. The networks were laid out such that articles (represented by the network's nodes) that possessed strongly correlated vocabularies were positioned closer to each other spatially than articles with weakly correlated vocabularies. We identified distinct research communities within the networks by locating and interpreting the clusters of lexically similar articles. We found that the Psychological Review was in some turmoil during this period compared with its first 15 years attributable, first, to Baldwin's unexpected departure in 1910; second, to the pressures placed on the discipline by United States entry into World War I; and, third, to the emergence of specialty psychology journals catering to research communities that had once published in the Review. The journal emerged from these challenges, however, with a better-defined mission: to serve as the chief repository of theoretical psychology in the United States.

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.127
GPT teacher head0.444
Teacher spread0.318 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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