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Record W2000890507 · doi:10.3138/cras.2013.036

Compassionate Readership: Anger and Suffering in Sapphire’s <i>Push</i>

2014· article· en· W2000890507 on OpenAlexvenueno aff
Heather Hillsburg

Bibliographic record

VenueCanadian Review of American Studies · 2014
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCompassionAngerBlamePsychologyPrivilege (computing)NarrativePsychoanalysisWhite privilegeSocial psychologyWhite (mutation)Power (physics)Gender studiesSociologyRace (biology)LiteratureArtLawPolitical science

Abstract

fetched live from OpenAlex

This article departs from polarizing academic discussions of compassion by exploring the relationship between anger and compassion in Sapphire’s novel Push. Martha Nussbaum explains that compassion can function as an ethical bridge that links one person to the next. Conversely, affect theorists, such as Lauren Berlant or Candace Vogler, argue that compassion reaffirms unequal relations of power, as individuals give or withhold their compassion when they see fit, leaving the circumstances that lead to suffering unaddressed. These discussions are complicated by the ongoing stereotypes that blame black women for their plight, leading people to withhold their compassion. Rather than expose these stereotypes as false constructs, this article focuses on the narrative pattern where Precious experiences egregious abuse, expresses intense anger at her suffering, and then longs for markers of privilege, such as white skin and affluence. It contends that Precious’s anger disrupts passive compassion, reminding readers of vectors of identity such as race, class, and body size that simultaneously give rise to and erase Precious’s suffering.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.367
Teacher spread0.307 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2014
Admission routes1
Has abstractyes

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