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Record W4313224676 · doi:10.24193/ekphrasis.28.3

Searching for Something in Sci-Fi Movies

2022· article· en· W4313224676 on OpenAlexaboutno aff
Ion Indolean

Bibliographic record

VenueEkphrasis · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyEPICNarrativeAlienOperaMovie theaterContext (archaeology)Space (punctuation)Point (geometry)LiteratureArt historyHistoryArtSociologyPhilosophy

Abstract

fetched live from OpenAlex

This article seeks to understand the way in which the Canadian director Denis Villeuneve sees his protagonist, Paul Atreides from Dune (2021)-based on the eponymous novel by Frank Herbert, as a starting point for almost every successful sci-fi movie in history: a good story, full of theological myths, where a chosen one has to demonstrate his virtues, in spite of all the evil lures. After establishing this narrative context, the article embarks on a mission to observe how the space epic genre (better known as space opera) established itself in cinema and how it has come to look now, by analyzing movies from successful series such as Blade Runner and Alien.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.004
Scholarly communication0.0090.005
Open science0.0000.002
Research integrity0.0010.001
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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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
Published2022
Admission routes1
Has abstractyes

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