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Record W2734499261 · doi:10.14288/cl.v0i225.187334

Alzheimer’s, Ambiguity, and Irony: Alice Munro’s “The Bear Came over the Mountain” and Sarah Polley’s Away from Her

2015· article· en· W2734499261 on OpenAlexaff
Marlene Goldman, Sarah Powell

Bibliographic record

VenueOpen Collections · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassionsIdentity (music)Embodied cognitionIronyMeaning (existential)AmbiguityNarrativePhilosophyArtAestheticsLiteratureEpistemologyLinguistics

Abstract

fetched live from OpenAlex

By offering an extended close reading of Alice Munro’s “The Bear Came over the Mountain” and Sarah Polley’s filmic adaption of this story, Away from Her, this paper traces the process whereby Munro’s and Polley’s narratives expand our understanding of the Lockean view of identity as “consciousness inhabiting a body.” More precisely, Munro’s and Polley’s texts shed light on Locke’s lesser known insights into the fraught relationship between memory and passions. By underscoring both the passionate, affective and embodied facets of remembering and forgetting and the intersubjective basis of meaning and identity, Munro’s and Polley’s works challenge Locke’s basic conception of an autonomous, rational self. In the process, both the story and the film deconstruct biomedical, mechanistic models by exposing the ironic instabilities and ambiguities associated with the experience of late-onset cognitive decline.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.015
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.273
Teacher spread0.224 · 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
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

Citations3
Published2015
Admission routes1
Has abstractyes

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