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Record W2101895026 · doi:10.1002/meet.14505001111

A case study of surrogate memory cues, self‐narrative, and recall

2013· article· en· W2101895026 on OpenAlexafffund
Lynne C. Howarth

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2013
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaAlzheimer Society
KeywordsRecallNarrativeSession (web analytics)PsychologyFree recallAutobiographical memoryCognitive psychologyComputer scienceLiteratureArt

Abstract

fetched live from OpenAlex

Abstract This poster session paper reports on research exploring (1) how individuals with early‐stage Alzheimer's disease (AD) use memory cues in the form of representations (tokens) to recall memories of life stories, and (2) ways in which representations (tokens) influence the nature and content of the individual's recall narrative. Further, it examines whether memory recall differs in response to personal, participant‐chosen memory cues, as compared to those selected by someone other than the participant. Reliance on personal artifacts used during two of three unstructured interview sessions resulted in recollections that seemed more scripted in delivery and circumscribed in detail. In contrast, and in response to researcher‐selected tokens used exclusively during session 3, full recollections and additional stories were seen to be more fully formed and detailed. While this subset of a larger study deals only with one individual, findings suggest that generic associations may be at least equal to, if not more effective than, unique, individuated artifacts to engendering creative self‐expression and vivid personal recall for those experiencing the initial memory loss of AD. This finding may open opportunities to cultural heritage institutions (libraries, archives, museums, galleries) to assemble information “memory boxes” that reinforce recall of life stories by clients with early‐stage AD.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.016
GPT teacher head0.298
Teacher spread0.282 · 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 designQualitative
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
Published2013
Admission routes2
Has abstractyes

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Same venueProceedings of the American Society for Information Science and TechnologySame topicIdentity, Memory, and TherapyFrench-language works237,207