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Record W7026432374

Adaptación del humor en el doblaje latinoamericano de Adventure Time (2010-2018)

2024· dissertation· es· W7026432374 on OpenAlexfundno aff

Bibliographic record

VenueRepositorio Académico UPC (Universidad Peruana de Ciencias Aplicadas) · 2024
Typedissertation
Languagees
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsAdventureContext (archaeology)Print media
DOInot available

Abstract

fetched live from OpenAlex

Adventure Time (2010-2018) es una serie animada creada por Pendleton Ward que sigue las aventuras de Finn el humano y Jake el perro en la Tierra de Ooo. Aunque las primeras temporadas de Adventure Time presentan argumentos infantiles, su trama se complejiza conforme se desarrolla la historia. Con respecto a su doblaje latinoamericano, este fue realizado por dos estudios: Sensaciones Sónicas (2010-2013), que empleó mexicanismos, y SDI Media (2014-2018), que utilizó español neutro. La presente investigación busca analizar el fenómeno de la adaptación del humor en el doblaje al español latino a partir del empleo de regionalismos y español neutro, y su impacto en la función humorística de cada versión. Se parte del supuesto base de que la construcción del humor en Adventure Time mezcla elementos de sus principales géneros narrativos: comedia de situación, fantasía, coming of age y postapocalipsis. Asimismo, se asume que Sensaciones Sónicas usó mexicanismos para lograr una buena recepción en la audiencia meta, mientras que SDI Media optó por español neutro para alcanzar a un público más amplio y mantener la construcción humorística de la serie en su idioma original.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.292
Teacher spread0.280 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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