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Record W7025520897

The Weight of Things: Inheritance and Gender in Alice Munro's Short Fiction

2025· article· en· W7025520897 on OpenAlexaboutno aff

Bibliographic record

VenueW&M Publish (College of William & Mary) · 2025
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsnot available
Fundersnot available
KeywordsInheritance (genetic algorithm)Alice (programming language)Identity (music)Presentation (obstetrics)Dynamics (music)
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines the role of the material world in Alice Munro’s short fiction, particularly as it relates to gender roles imposed on women and girls in mid-twentieth-century rural Southern Ontario. In analyzing a body of work, including two short stories from the 2001 collection Hateship, Friendship, Courtship, Loveship, Marriage: “Family Furnishings” and “Hateship, Friendship, Courtship, Loveship, Marriage,” the study employs a new-materialist framework, informed by Bill Brown’s definition of “Things” as more dynamic and personal than what we consider objects. It uses close textual analysis to explore the relationship between the dynamic role of material things, especially inherited material things, and Munro’s comments on the female body and women’s role in society. The findings suggest that inherited material objects convey inherited gendered expectations of domesticity, and their aggressive behavior in the text evokes a hostile domestic world, one in which dynamics of ownership and inheritance complicate the presentation of gender roles in the text. This analysis sheds light on Munro’s portrayal of the complexities of female identity and domestic life.

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)0.000
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.212
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.014
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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