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Record W1933307501

가상 프린트 비용(VPF) 도입에 의한 국내 디지털 스크린 보급과 문제점 연구

2013· article· ko· W1933307501 on OpenAlexaboutno aff
박지홍

Bibliographic record

Venue영화연구 · 2013
Typearticle
Languageko
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterSoftware deploymentDigital Millennium Copyright ActBusinessBankruptcyFilm industryComputer scienceMultimediaComputer graphics (images)AdvertisingArtPolitical scienceVisual artsIntellectual propertyLawFinance
DOInot available

Abstract

fetched live from OpenAlex

Eastman Kodak is well-known for film manufacturer but in fact also has considerably contributed to the advancement of digital cinema. Thus, this study summarizes the progress of digital image processing technology vigorously led by Kodak in and around its Cineon System which was created to support the work flow of digital intermediate (DI) film production. Then, the mechanism of Virtual Print Fee (VPF) and its application in the United States and Canada are examined. VPF is a key concept of digital cinema distribution and has been playing a crucial role in the rapid expansion of digital screens throughout the world. It is a kind of financing mechanism for supporting movie theaters to switch their existing film projectors to the new digital projection systems through redistributing the savings realized by distributors as replacing film prints with digital prints, the so-called Digital Cinema Package (DCP). Lastly, the introduction of VPF, the deployment of digital projection systems and the distribution of digital cinema in the Korean film industry are closely investigated. Meanwhile, the problems which had already been occurred or posed in those rather abrupt processes are also discussed. The purpose of this essay is to raise the issues related to ‘cinematic diversity’ to prevent a few giant corporations from monopolizing the market and to protect quite a few small businesses at risk of bankruptcy. It also expects to bring up some sorts of movements among the Korean filmmakers and others to keep the right of choice or the options both in production and distribution. One of the options may be the “celluloid” film.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.019

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 head0.033
GPT teacher head0.325
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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