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Is Religion Compatible with Media Entertainment?

2014· book-chapter· en· W2480666048 on OpenAlexaff
Guy Marchessault

Bibliographic record

VenueAdvances in religious and cultural studies (ARCS) book series · 2014
Typebook-chapter
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsEntertainmentThe artsStorytellingAestheticsMeaning (existential)DanceNarrativeRhetoricRelation (database)ArtSociologyLiteratureVisual artsEpistemologyPhilosophyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Is it possible to reconcile the spectacular approach of the media with the inner nature of the spiritual? Can one imagine the presence of religions within an ambience of entertainment? There always were tensions between religions and plays and games. Religions feared theatre, play, music, arts, dance, cards, and media, of course. Why are play and entertainment considered to be so dangerous? Would it be a better approach to discern true spiritual openings through play, and through media entertainment? In this chapter, the authors discuss the point of views of an historian, a film director, communication researchers, a philosopher, sociologists, and anthropologists, who offer a refined understanding of the capacity of playing to reveal the human search for meaning and spiritual journey. Play, and certainly media entertainment, can open humans to their own various potentialities, giving significance to their relation with the world and with other humans, and so with the sacredness. However, this can be done only if one respects the typical languages of the media made out of narratives and storytelling, which implies capacity of creativity in arts and rhetoric, combined with respect for ethical and spiritual dimensions of believers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0060.006
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.263
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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