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Record W4229444034 · doi:10.46630/jkal.2022.21

ALTERNATIVE LITERARY HISTORIES: TONI MORRISON’S A MERCY AND COLONIAL NORTH AMERICA

2022· book-chapter· en· W4229444034 on OpenAlexaff
Ana Kocić Stanković

Bibliographic record

VenueFilozofski fakultet u Nišu eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAnglo American (Canada)
Fundersnot available
KeywordsColonialismMainstreamWhite (mutation)HistoryInterpretation (philosophy)ImmigrationSubject (documents)American historyReading (process)Gender studiesLiteratureSociologyArtPolitical scienceLawAncient historyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

The paper offers a reading of Toni Morrison’s 2008 novel A Mercy as an alternative history of colonial North America focused on empowering the powerless and giving a voice to those silenced and overlooked in mainstream histories. The theoretical framework is based on Hayden White’s idea of history as inevitably imbued with fictionalized elements, Homi Bhabha’s interpretation and Morrison’s idea of ‘rememory’ and the black feminist idea of acquiring one’s own voice by becoming the speaking subject. The main thesis is that Morrison’s book offers an alternative version of early American history by giving voices to the representatives of various minority groups from the colonial period of North America: white immigrant, African American, mixed-race and Native American women and indentured servants. Using multiple narrators, Morrison not only manages to complement ‘official’ history books but also to use individual narrators as symbols of the collective experiences of their respective groups.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.968
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.003
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 head0.017
GPT teacher head0.273
Teacher spread0.255 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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