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Record W2966527142 · doi:10.1093/alh/ajz024

Cormac McCarthy, Marilynne Robinson, and the Responsibility to Protect

2019· article· en· W2966527142 on OpenAlexfundno aff
Spencer Morrison

Bibliographic record

VenueAmerican Literary History · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
FundersKillam Trusts
KeywordsDoctrineHarmHuman rightsSubject (documents)Environmental ethicsCriticismPoliticsContext (archaeology)ConsciousnessIntervention (counseling)AmbiguitySociologyPolitical scienceEpistemologyLawPhilosophyHistoryPsychology

Abstract

fetched live from OpenAlex

Abstract This essay describes a new context for understanding the political stakes of US fiction described as postsecular—namely, the emergence of global human rights consciousness in the later twentieth century. Placing Americanist literary criticism’s recent “religious turn” in dialogue with the field of literature and human rights yields new insights for each, I argue. To demonstrate the benefits of this critical dialogue, I interpret two major novels studied by the “religious turn”—Cormac McCarthy’s The Road and Marilynne Robinson’s Gilead—in relation to the United Nations’ responsibility to protect doctrine, which has reshaped the concept and practice of humanitarian intervention in the twenty-first century. Each novel dramatizes a dying father’s strained deliberations over the ethics of intervention on behalf of a vulnerable child—the subject whose maturation provides the figural foundation for human rights consciousness—against potentially grave harm. Each, moreover, deploys language and concepts of spiritual ambiguity to illuminate ethical and epistemological dilemmas that beset decisions regarding whether or not to intervene. Locating sacredness in the subject of human rights, McCarthy’s and Robinson’s texts enmesh rights claims and spiritual idioms in ways that suggest new critical paths for both Americanists and scholars of human rights and literature.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.180
Teacher spread0.175 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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