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Record W7116645512

The Siamese Self in Barbara Gowdy’s “Sylvie”

2025· article· en· W7116645512 on OpenAlexaboutno aff
Jennifer Murray

Bibliographic record

VenueOpenEdition (OpenEdition) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsPropositionRomancePerspective (graphical)SisterSituatedCharacter (mathematics)Object (grammar)
DOInot available

Abstract

fetched live from OpenAlex

Barbara Gowdy, one of the lesser-studied Canadian writers of short stories, is the author of We So Seldom Look on Love (1997). This collection highlights figures of the grotesque, where transgression, indetermination, and excess are modalities that allow her to explore the complexities of being human. In “Sylvie,” the eponymous main character is doubled through the Siamese twin attached to her abdomen in the form of two small, hanging legs: her sister ‘Sue.’ This image of the self, doubled specifically at the level of the pelvis, allows for the exploration of excess as it figures in the Lacanian perspective on female sexuality. Following Copjec, a Lacanian critic who asserts that a woman “lives herself—enjoys her body—as if it were not her own but another’s, as if she were the double of another,” this contribution means to demonstrates that Sue functions as a libidinal part-object that is constitutive of Sylvie’s sense of self and her ability to desire. This proposition leads—through questions of doubling, memory, seeing and being seen—in an exploration of how ‘Sue’ and ‘Sylvie’ are situated in the discourses of various others, from parental figures to indifferent others, and finally tragically, to a significant romantic love interest.

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)0.001
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 score0.379
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.018
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.232
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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