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Narrative Gerontology

2015· other· en· W4250518805 on OpenAlexaff
Gary M. Kenyon

Bibliographic record

VenueThe Encyclopedia of Adulthood and Aging · 2015
Typeother
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsSt. Thomas University
Fundersnot available
KeywordsNarrativeMeaning (existential)Narrative inquirySociologyAestheticsPsychologyArtLiteraturePsychotherapist

Abstract

fetched live from OpenAlex

Narrative gerontology provides a lens through which to see the storied nature of aging. A basic assumption of narrative gerontology is that the biographical side of human life, that is, biographical aging, is as complicated and as critical to explore as is the biological side. Human beings not only have life stories; we are stories. The most important issue centers on how meaning is communicated in the stories of our lives, and the way in which giving voice to that meaning helps us better understand how a life has been lived, how it is lived, and how it can be lived. Narrative gerontologists argue that, in principle, it is always possible for lives to be re‐storied. There are particular theoretical, research, and practice issues, that is, narrative care , to which these insights are applied, from wisdom to time to dementia to healthcare.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.006
Scholarly communication0.0130.011
Open science0.0010.012
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0690.019

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 head0.040
GPT teacher head0.368
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2015
Admission routes1
Has abstractyes

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