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Record W2617276901 · doi:10.1002/acp.3314

Adult Eyewitness Memory for Single Versus Repeated Traumatic Events

2017· article· en· W2617276901 on OpenAlexaff
Tjeu P. M. Theunissen, Thomas Meyer, Amina Memon, Camille C. Weinsheimer

Bibliographic record

VenueApplied Cognitive Psychology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyEyewitness testimonyEyewitness memoryEvent (particle physics)Test (biology)Developmental psychologyAudiologyClinical psychologyCognitive psychologyRecallMedicine

Abstract

fetched live from OpenAlex

Summary Reports from individuals who have witnessed multiple, similar emotional events may differ from reports from witnesses of only a single event. To test this, we had participants (N = 65) view a video of a road traffic accident. Half of the participants saw two additional (similar) aversive films. Afterwards, participants filled out the Self‐Administered Interview on the target film twice with an interval of 1 week. Participants who saw multiple similar films were less accurate in recalling details from the target film than participants in the control condition. On their second report, participants were less complete but more accurate compared with their first report. These results indicate that adults who have witnessed multiple repeated events may appear less reliable in their reports than adults who have witnessed a single event. These findings are relevant when evaluating eyewitness evidence and call for new approaches to questioning witnesses about repeated events.Copyright © 2017 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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.149
GPT teacher head0.400
Teacher spread0.251 · 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 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

Citations34
Published2017
Admission routes1
Has abstractyes

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