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Record W2598779427 · doi:10.3138/tjt.2016-0017

For the Love of (Black) Christ: Embracing James Cone's Affective Critique of White Fragility

2017· article· en· W2598779427 on OpenAlexvenueno aff
Marvin E. Wickware

Bibliographic record

VenueToronto Journal of Theology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInterdisciplinary Cultural and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)SociologyRepentanceEconomic JusticeHappinessAestheticsPhilosophyCone (formal languages)TheologyPsychoanalysisSocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

Throughout his written work, James H. Cone proclaims a message of liberation that takes into account both the sociopolitical structural and emotional dimensions of life together. Cone suggests that we cannot settle for—in fact, we cannot even have—love without justice, or justice without love. In this article, I look at Cone's body of work to uncover and reclaim what he performed, but did not explicitly theorize, in his pursuit of love and justice: the dismantling of white fragility, a white supremacist affective construct that shapes much Christian theology, including what seeks to further the cause of racial reconciliation. I then theorize the distorted love that Cone critiques, drawing on the affect theory of Sara Ahmed and the critical whiteness theory of Robin DiAngelo, finally suggesting an alternative vision of love that is consistent with Cone's theology. This alternative vision presents love as acceptance of one's radical, even fundamental, need for the beloved. In contexts of racial tension, this love does not guarantee happiness or comfort, but instead offers aid toward repentance and salvation. In this view, black love for white people cannot be expected to take the form of endless affirmation and encouragement, but instead should look like belief in the capacity of white people for moral and ethical action.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.361
Teacher spread0.338 · 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 designQualitative
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
Published2017
Admission routes1
Has abstractyes

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