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Record W2919179801 · doi:10.5406/amerjpsyc.132.1.0123

What Should We Believe?

2019· article· en· W2919179801 on OpenAlexaffabout
Keith Oatley

Bibliographic record

VenueThe American Journal of Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIconCitationDownloadLibrary sciencePublishingInformation retrievalWorld Wide WebArt historyPsychologyComputer scienceArtLiterature

Abstract

fetched live from OpenAlex

Book Review| April 01 2019 What Should We Believe? Belief: What It Means to Believe and Why Our Convictions Are So Compelling. By James E. Alcock. Amherst, NY: Prometheus Books, 2018. 638 pp. Hardcover, $28. Keith Oatley Keith Oatley Department of Applied Psychology and Human, Development, University of Toronto, 252 Bloor Street West, Toronto, M5S 1V6, Canada, E-mail: keith.oatley@utoronto.ca Search for other works by this author on: This Site Google The American Journal of Psychology (2019) 132 (1): 123–125. https://doi.org/10.5406/amerjpsyc.132.1.0123 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Keith Oatley; What Should We Believe?. The American Journal of Psychology 1 January 2019; 132 (1): 123–125. doi: https://doi.org/10.5406/amerjpsyc.132.1.0123 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.070
GPT teacher head0.436
Teacher spread0.366 · 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 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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