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Record W1481782158 · doi:10.1002/acp.3148

Disruptive Individual Experiences Create Lifetime Periods: a Study of Autobiographical Memory in Persons with Spinal Cord Injury

2015· article· en· W1481782158 on OpenAlexafffund
Tuğba Uzer, Norman Brown

Bibliographic record

VenueApplied Cognitive Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutobiographical memoryPsychologySpinal cord injuryPeriod (music)Injury preventionDevelopmental psychologyPoison controlCognitive psychologySpinal cordRecallMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Summary Previous research showed that transitional events causing catastrophic and long‐lasting changes in group of people's lives (e.g., wars) create autobiographical periods. We investigated whether spinal cord injury (SCI), an involuntary and externally driven disruptive event at the individual level, would also act as a temporal landmark and spawn personal periods and whether these periods have comparable functions and temporal characteristics as those generated at the group level. Thirteen volunteers with SCI recalled a cue‐related autobiographical event for each of 22 cue words. Later, participants thought aloud when dating each event. We used the prevalence of injury‐related references as an index of the degree to which spinal cord injury affects people's lives. We found frequent references to injury for the period neighboring injury event. Unexpectedly, we also found that SCI resulted in post‐injury decrease in event memory. Results imply that SCI provides a temporal landmark and creates an autobiographical period.Copyright © 2015 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)0.004
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.393
Teacher spread0.337 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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