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Record W2333821735 · doi:10.1177/1471301213476505

Dementia, personhood and embodiment: What can we learn from the medieval history of memory?

2013· article· en· W2333821735 on OpenAlexafffund
Stephen Katz

Bibliographic record

VenueDementia · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsTrent University
FundersCanadian Institutes of Health Research
KeywordsPersonhoodNeurocognitiveDementiaEmbodied cognitionPsychologyCognitionValue (mathematics)Memory impairmentCognitive scienceNeuroscienceMedicineEpistemologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

Memory and dementia are historical ideas that preceded the development of modern neuroscientific, psychogeriatric and medical approaches to aging and cognitive impairment. This article explores the value of such historical ideas in order to understand the discourses and metaphors by which Western thought has individualized memory as the guarantor of rational personhood, while at the same, treating memory decline as a threat to healthy and successful aging. Discussion focuses on the relationship between memory and the body in the classical and medieval ars memoria (the art of memory) and in the early modern philosophies of personhood, particularly the work of John Locke. Conclusions consider the significance of Western culture's history of embodied memory as it moved from cosmic to individual to neurocognitive sites for our wider views about the treatment of dementia.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.050
Scholarly communication0.0070.013
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.197
Teacher spread0.171 · 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 designTheoretical or conceptual
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

Citations26
Published2013
Admission routes2
Has abstractyes

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