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Record W2919969018 · doi:10.5406/amerjpsyc.132.1.0119

A Guided Tour of Mind and Brain Well Worth Taking

2019· article· en· W2919969018 on OpenAlexaffabout
James E. Alcock

Bibliographic record

VenueThe American Journal of Psychology · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsYork University
Fundersnot available
KeywordsCitationIconDownloadLibrary sciencePublishingPsychologyWorld Wide WebComputer scienceArtLiterature

Abstract

fetched live from OpenAlex

Book Review| April 01 2019 A Guided Tour of Mind and Brain Well Worth Taking Our Minds, Our Selves: A Brief History of Psychology. By Keith Oatley. Princeton, NJ: Princeton University Press, 2018. 376 pp. Hardcover, $29.95. James Alcock, PhD James Alcock, PhD Professor of Psychology, Glendon College, York University, 2275 Bayview Avenue, Toronto, M4N 3M6, Canada, E-mail: jalcock@yorku.ca Search for other works by this author on: This Site Google The American Journal of Psychology (2019) 132 (1): 119–122. https://doi.org/10.5406/amerjpsyc.132.1.0119 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation James Alcock; A Guided Tour of Mind and Brain Well Worth Taking. The American Journal of Psychology 1 January 2019; 132 (1): 119–122. doi: https://doi.org/10.5406/amerjpsyc.132.1.0119 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressThe American Journal of Psychology Search Advanced Search The text of this article is only available as a PDF. Copyright 2019 by the Board of Trustees of the University of Illinois2019 Article PDF first page preview Close Modal You do not currently have access to this content.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0400.025

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 head0.043
GPT teacher head0.322
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2019
Admission routes2
Has abstractyes

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