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Record W4309840637 · doi:10.3390/rel13121131

E-Word? McLuhan, Baudrillard, and Verisimilitude in Preaching

2022· article· en· W4309840637 on OpenAlexaff
Michael P. Knowles

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsMcMaster UniversityMcMaster Divinity College
Fundersnot available
KeywordsIncarnationRevelationIdentity (music)Representation (politics)MediationPhilosophyVerisimilitudeTheologySociologyDehumanizationEpistemologyAestheticsLawAnthropology

Abstract

fetched live from OpenAlex

Electronic communication of the Christian message—online preaching—raises distinct theological challenges. Notwithstanding the undeniable convenience and unlimited geographical reach of “virtual church”, electronic media have the potential to separate preacher from congregants, congregants from one another, and—potentially of greatest concern—the church from God, even while appearing to accomplish the opposite. Communication theorist Marshall McLuhan (1911–1980) argues provocatively that virtual representation is at the cost of authentic human identity (in which case it is inimical to community), while French sociologist and philosopher Jean Baudrillard (1929–2007) warns of substituting representation for reality, especially in matters of theology and the identity of God. The paradigm of Jesus’ Incarnation, by contrast, mandates un-mediated divine-human and human-to-human communication, requiring engagement between persons themselves rather than their avatars or provisional simulacra. With respect to electronically mediated communication itself, acknowledging divine initiative in the formation of identity (as a feature of soteriology) and of understanding (under the category of revelation) countermands the more dehumanizing and anti-theological influences that McLuhan and Baudrillard both identify, encouraging direct engagement with God in the person of the Holy Spirit rather than resorting to technological mediation.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.525

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 studies0.0010.000
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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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 designTheoretical or conceptual
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

Citations3
Published2022
Admission routes1
Has abstractyes

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