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Record W2545825542 · doi:10.1002/acp.3267

Commentary on Brewin and Andrews

2016· article· en· W2545825542 on OpenAlexaff
D. Stephen Lindsay, Ira E. Hyman

Bibliographic record

VenueApplied Cognitive Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAutobiographical memoryPsychologyFalse memoryContext (archaeology)Cognitive psychologyEyewitness testimonyExperimental psychologyEpistemologySocial psychologyCognitionRecallHistoryPsychiatry

Abstract

fetched live from OpenAlex

Summary Brewin and Andrews (2016) reviewed the literature on false memories of autobiographical events, discussed those findings in the context of theoretical accounts of autobiographical memory, and concluded that no more than 15% of people exposed to suggestive influences come to develop robust false memories of significant childhood events. We note several points on which we agree with Brewin and Andrews. But we also take issue with their review and their conclusions. The likelihood of false memories depends on interactions among numerous variables, and psychology is far short of the degree of theoretical precision required to specify any upper limit. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.020
metaresearch head score (Gemma)0.159
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.064
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.159
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.003
Science and technology studies0.0070.015
Scholarly communication0.0090.017
Open science0.0110.007
Research integrity0.0640.114
Insufficient payload (model declined to judge)0.0110.008

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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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
GenreCommentary

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

Citations7
Published2016
Admission routes1
Has abstractyes

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