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Contemporary Narratives of Senility

2020· article· en· W3087248507 on OpenAlexfundno aff
Maija Könönen

Bibliographic record

VenueLaboratorium Russian Review of Social Research · 2020
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersKoneen SäätiöQueen's UniversityMcGill University
KeywordsNarrativeSociologyNarratologyPerceptionAffect (linguistics)Literary criticismPerspective (graphical)AestheticsPhenomenonLiteratureHistoryGender studiesPsychologyArtVisual artsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This essay explores the various ways of talking about senility and how the two competing (or, possibly, complementing) discourses—the biomedical dementia discourse and the discourse of senility as part of “normal” aging—affect our perception of and attitudes toward old age. Moreover, I explore the role of fiction in articulating senility. As my approach combines critical gerontology with narratological analysis, it belongs to the burgeoning domain of literary gerontology, a discipline that embraces various literary genres from fiction to nonfiction. This double perspective of literary studies and cultural gerontology makes it possible to examine senility as a historically and culturally specific concept and phenomenon. My aim is to demonstrate with two examples from contemporary Russian short prose (Nina Katerli’s story “Na dva golosa” [In Two Voices] and Nina Sadur’s story “Stul” [The Chair]) how a literary work can be related to prevailing cultural, sociological, and medical discourses on and norms of aging. With tools of narratology I shed light on the literary devices deployed in the stories to articulate the experience of senility from the viewpoint of the elderly protagonists themselves. Text in English DOI: 10.25285/2078-1938-2020-12-2-169-186

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.225
GPT teacher head0.507
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.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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