MétaCan
Menu
Back to cohort
Record W1960212019 · doi:10.18002/10612/4615

La evolución del supervillano en el "comic book" norteamericano: de Superman a Watchmen

2015· dissertation· es· W1960212019 on OpenAlexaff
Miguel Ángel Morán González

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsExtendicare (Canada)
Fundersnot available
KeywordsComicsSupermanArtHumanitiesArt historyLiterature

Abstract

fetched live from OpenAlex

La presente tesis doctoral es un estudio diacronico sobre el origen y evolucion del supervillano en el arte secuencial y dentro del formato del comic-book norteamericano. El trabajo parte del origen de los superheroes en el comic a partir de Superman (1938) y de los personajes antagonistas, que no eran supervillanos sino villanos. La tesis se centra en los principales oponentes de los superheroes mas emblematicos de los personajes de DC Comics y de la Marvel Comics, comenzando por Superman y despues por Batman y Spider-Man hasta llegar a la deconstruccion del superheroe en la novela grafica Watchmen, del escritor Alan Moore y del dibujante Dave Gibbon. Asimismo, el trabajo pretende dar respuesta a la influencia que los distintos equipos creativos de comics han seguido para crear a supervillanos: la mitologia, la novela negra y de terror, asi como la novela de ciencia ficcion y los propios comics serializados a traves de las tiras diarias (Daily Strips) y paginas dominicales (Sunday Pages). En conclusion, el supervillano es un elemento clave para entender el genero de superheroes. Es el personaje que se contrapone al superheroe por incumplir las normas establecidas por la sociedad: usa metodos violentos, la sociedad los desprecia, se reinventan o modifican conviertiendose en elementos recursibles porque reaparecen esporadicamente en una coleccion. Sin embargo, la maldad de un supervillano nunca es absoluta, todos tienen cierto grado de sentimientos y emociones

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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.

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 routes1
Has abstractyes

Explore more

Same topicComics and Graphic NarrativesFrench-language works237,207