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Record W4406772558 · doi:10.56855/jllans.v2i1.280

Educate Children's Character With Finding Nemo Film Media

2023· article· en· W4406772558 on OpenAlexaboutno aff
Rida Patria

Bibliographic record

VenueJournal of Literature Language and Academic Studies. · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)ArtPsychologyMathematics

Abstract

fetched live from OpenAlex

Children as the next generation of the nation, need to get serious attention, because the progress of a country will depend on the current and future generations. In this article, the author will conduct a review of an animated children's film Finding Nemo as a positive viewing medium to educate children's character as the nation's successor. Finding Nemo is a computer graphic animated film made in the United States which won an Academy Award. Released on May 30, 2003 in Canada and the United States. The main players are Albert Brooks, Ellen DeGeneres, Alexander Gould, Willem Dafoe, and many more. The director is Andrew Stanton. The method used in this research is descriptive-analytical method, in which the researcher describes all the data or conditions of the subjects or research objects and then analyzes them by explaining all the aspects contained in the film in the form of dialogue. One of the most important moral messages in this film is the importance of following the advice of parents.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.411
Teacher spread0.359 · 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 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
Published2023
Admission routes1
Has abstractyes

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