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Record W2889439896 · doi:10.1002/acp.3454

Measuring the effectiveness of the sketch procedure for recalling details of a live interactive event

2018· article· en· W2889439896 on OpenAlexaff
Joseph Eastwood, Brent Snook, Kirk Luther

Bibliographic record

VenueApplied Cognitive Psychology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsMemorial University of NewfoundlandOntario Tech University
FundersMedical Research Council
KeywordsSketchRecallCognitive interviewPsychologyInterviewEvent (particle physics)Context (archaeology)Free recallCognitive psychologyAction (physics)Control (management)Social psychologyCognitionComputer scienceArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

Summary The effectiveness of a sketch procedure for enhancing the recall of a live interactive event was assessed. Participants (N = 88) engaged in an interaction with a confederate, were administered a sketch, mental reinstatement of context (MRC), or control procedure and then asked to recall the experienced event. Results showed that participants who were administered a sketch procedure recalled more correct details than those administered an MRC or control procedure (d = 0.55 and d = 1.31, respectively). The increased recall was seen primarily for action and object details, with little difference between procedures for recall of person and verbal details. In addition, the effect of interview procedure on the number of incorrect details recalled was nonsignificant. The utility of the sketch procedure for investigative interviewing is discussed.

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)0.005
metaresearch head score (Gemma)0.041
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.353
Teacher spread0.291 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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