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Record W2108157966 · doi:10.7202/044168ar

Reading Beyond Race in Margaret Laurence’s “The Loons” from A Bird in the House

2010· article· en· W2108157966 on OpenAlexafffundvenue
Nora Foster Stovel

Bibliographic record

VenueInternational Journal of Canadian Studies · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsUniversity of Alberta
FundersMcMaster University
KeywordsContext (archaeology)Race (biology)NarrativeRacismReading (process)HistoryGender studiesArtSociologyLiteraturePhilosophyLinguisticsArchaeology

Abstract

fetched live from OpenAlex

Margaret Laurence’s short story “The Loons” from A Bird in the House (1970) has proved highly controversial as an example of racist or anti-racist literature. The question is which? Laurence’s bifocal narrative technique allows the mature narrator Vanessa MacLeod to revise her childhood prejudices and satirize the racial stereotypes in her ten-year-old self’s perception of Piquette Tonnerre. Some critics have opined that, in the last sentence of the story, Laurence reinscribes the racism she has taken pains to deconstruct. Considering the story in context, however, suggests the focus of the story is the death of Vanessa’s father. Realizing Piquette shares her grief inspires Vanessa to view her, years later, as an individual. The loons, “those phantom birds,” prophesy death. Vanessa realizes Piquette, who suffered so greatly, “might have been the only one, after all, who had heard the crying of the loons.”

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)0.003
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 score0.864
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0190.013
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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
Published2010
Admission routes3
Has abstractyes

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