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Donnie Darko: Imagem, Tecnologias Digitais e Multimediaçao em um Filme entre o Underground e o Massivo

2005· article· pt· W2277486677 on OpenAlexaff
Erick Felinto

Bibliographic record

VenueRevista Contracampo · 2005
Typearticle
Languagept
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

A experiência cultural contemporânea parece recusar a possibilidade dos desenhos nítidos e das definições acabadas. Oposições tradicionais como erudito ou massivo e comercial ou experimental perdem sua consistência e dão lugar a uma poética da imprecisão, do hibridismo e da ruptura de fronteiras. O obscuro Donnie Darko (2001), filme de estreia do diretor Richard Kelly, oferece excelente campo para a exploração dessa poética híbrida, dado que trabalha tais ambiguidades em uma multiplicidade de níveis, desde sua indefinição em termos de gênero (ficção científica, drama adolescente, terror, ficção historiográfica, etc) até sua hesitação quanto à natureza cultural (cinema experimental, paradigma hollywoodiano). O objetivo deste trabalho é investigar os desdobramentos da poética da imprecisão em um filme de relativo sucesso comercial, explorando especialmente o papel das tecnologias digitais na configuração de um território híbrido e multimidiático, que envolve não apenas o filme em si, mas também os produtos que complementam sua narrativa, como seu website oficial, salas de discussão e extras do DVD.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.023
GPT teacher head0.233
Teacher spread0.210 · 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 designQualitative
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

Citations1
Published2005
Admission routes1
Has abstractyes

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