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Record W2093555753 · doi:10.1163/156851506776145733

History and Pseudo-History in the Jesus Film Genre

2006· article· en· W2093555753 on OpenAlexaboutno aff
Adele Reinhartz

Bibliographic record

VenueBiblical Interpretation · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFaithTemptationHistoricity (philosophy)Historical JesusNew TestamentLiteraturePhilosophyIronyBiblical studiesHistoryArtTheologyReligious studies

Abstract

fetched live from OpenAlex

Abstract Movies in the Jesus film genre often claim to be not only faithful renditions of their texts—the New Testament Gospels—but also accurate representations of the person, words and deeds of the historical Jesus himself. More fundamentally, they also presume a tight connection between historicity and faith. A viewer who learns about the historical Jesus through these films, they suggest, will have his or her faith forever strengthened. The irony is that whereas the Gospels have inspired profound ideas and beliefs that have shaped Christian spiritually through the two millennia since Jesus' lifetime, their transformation on the silver screen almost always results in a superficial, shallow, simplistic representation of Jesus, his life and his significance for humankind. While almost every Jesus movie has its moments of grace and artistry, most of them plod through the story even as they claim to bring to life both the Jesus of history and the Christ of faith.This paper explores two films—Martin Scorsese's The Last Temptation of Christ (1988) and Denys Arcand's Jesus of Montreal (1989)—that break out of this pattern, and in doing so mount a fundamental and explicit challenge to the links between scripture, history and faith. Paradoxically, this challenge allows them a more profound and nuanced exploration of Christian faith than can be found in most other films of this genre.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.256

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 head0.031
GPT teacher head0.220
Teacher spread0.189 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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