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Record W2938938199 · doi:10.1163/23523085-00501004

Remediating Kalimán: Digital Evolutions of Eugenic Agents

2019· article· en· W2938938199 on OpenAlexaff
Itzayana Gutiérrez

Bibliographic record

VenueAsian Diasporic Visual Cultures and the Americas · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsComicsEugenicsPopular cultureClothingWhite (mutation)Punishment (psychology)Character (mathematics)Reality tvHistoryArtSociologyMedia studiesArt historyLiteraturePolitical sciencePsychologyLawSocial psychology

Abstract

fetched live from OpenAlex

Kalimán is a Mexican superhero that has circulated Orientalist eugenic values for over fifty years across Latin America. Although Indian, and wearing traditional Indian subcontinental clothing, distinguishable only by a jewel-encased “K” on his turban, Kalimán is a muscular, blue-eyed, and white character. He was created in 1963 as the main protagonist of a radio series that spawned a comic magazine in 1965, two films in 1972 and 1976, and animations and video games in the early 2010s, in a massive process of remediation that has guaranteed a solid mark in the cultural patrimony of the Americas. Since Kalimán incarnates impulses of punishment and desire over racially contaminated brown and black characters, his undisturbed, easy-to-access, and enduring presence provides evidence of deeply ingrained anti-Asian violence in Latin American popular culture, as well as the urge to develop a critical look at graphic violence traditions which continue to be treasured.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.010
Scholarly communication0.0120.009
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.015
GPT teacher head0.326
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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