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Record W3025001208 · doi:10.3138/jrpc.2017-0053

Corpus Christi, Corpus Cyborgensis, and the Body Politic: The Passion Play of <i>RoboCop</i>

2020· article· en· W3025001208 on OpenAlexaffvenue
John A. Geck

Bibliographic record

VenueJournal of Religion and Popular Culture · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBody politicMetaphorPassionPostmodernismScholarshipLiteraturePower (physics)ManifestoPhilosophyArtArt historyTheologyPoliticsLaw

Abstract

fetched live from OpenAlex

Director Paul Verhoeven describes his RoboCop (1987) as “an American Jesus,” but he also notes that “I don’t believe in the resurrection of Jesus in any way.” His irreverent satire echoes the “ironic blasphemy” of Donna Haraway’s “A Manifesto for Cyborgs” (1985) and other specifically postmodern theological readings of cyborg as saviour. Haraway’s more optimistic outlook is indirectly challenged by John O’Neill, who in his Five Bodies (1985) warns about losing human semiology to a capitalistic mechano-morphic society. Peter Travis (1987) uses O’Neill’s thesis to address the notion of the corpus Christi as the metaphor for the body politic in the late medieval English Corpus Christi pageants, and 1970s and 1980s scholarship on the famous Corpus Christi cycle plays of later medieval England generally focused on the role that having enacted Christ figures in the plays served to reunify a civic society devastated by economic and social turmoil following the Black Death. Verhoeven’s Christological imagery in RoboCop thus builds upon a long tradition of dramatic performances of the life of Christ. In close adherence to the larger contemporary social issues presented in the late medieval dramatic milieu, RoboCop successfully represents a Christ figure whose broken body reflects Regan-era civic weakness and decay while simultaneously possessing a revolutionary salvific power.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.284

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 studies0.0000.000
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 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
Published2020
Admission routes2
Has abstractyes

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