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Record W2240095055 · doi:10.29173/mruhr234

Comparison of Film Language and Historical Fact

2015· article· en· W2240095055 on OpenAlexaffvenue
Katherine Kowalewski

Bibliographic record

VenueMount Royal Undergraduate Humanities Review (MRUHR) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMetaphorCinematographyCharacter (mathematics)Similarity (geometry)Film genreFilm studiesFilm industryLinguisticsAestheticsHistoryArtMovie theaterLiteratureComputer scienceVisual artsArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Film language is an important aspect of analyzing film. Film language is the term used to break down the aspects of movies to develop the deeper meanings being communicated to the audience. The following essay studies three areas of film language in these historical movies: 12 Years a Slave, Nanking, and Frida. These three films will be examined through their use of dialogue, cinematography, and metaphor in the characters' actions. The essay compares the similarities and differences in the three films and offers a deep analysis of each film individually. Furthermore, a historical understanding is developed from the messages conveyed in the films. It is concluded that the movies share a similarity in their use of dialogue to convey character development. However, the difference in the three films is portrayed in their use of metaphor in the characters' actions to communicate various messages to the viewers. Through the study of the three historical films, we develop a greater understanding of the historical messages they express and a deep appreciation of the intricacy of the film industry.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.271
GPT teacher head0.419
Teacher spread0.148 · 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 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
Published2015
Admission routes2
Has abstractyes

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