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Record W4393163692 · doi:10.55016/ojs/muj.v2i1.78789

Coloring History

2024· article· en· W4393163692 on OpenAlexaff
Hannah Adriano

Bibliographic record

VenueThe Motley Undergraduate Journal · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory, Culture, and Diplomacy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Since its release, the Netflix show, Bridgerton, has been a hit series among its mass audiences as it harnesses an average of 103,550,000 views per season and currently holds fourth place in Netflix’s most popular English-language series (Tudum, 2024). The show has gained immense popularity not only because of its captivating plotlines but also because of the diverse cast of characters placed within the Regency Era. From this, it is critical to dissect and understand how race is represented within the show to discern the discourses being perpetuated to its viewers. Throughout this essay, I will employ Stuart Hall’s theory of representation and the politics of signification to unpack the racial representation within the Netflix show, Bridgerton, and how it reproduces colour-blind casting, interracial relationships, and dark versus light symbolism to perpetuate post-racial and Orientalist discourses. This analysis explores how colour-blind casting sustains post-racial discourses in the disguise of escapist media, which strips the Other of their lived experience. Moreover, it highlights how the depiction of interracial relationships within the show contributes to both post-racial and Orientalist discourses by placing the Occident over the Orient, and disregarding the prejudice often faced by interracial couples during that period. It further reveals how dark versus light symbolism is embedded throughout the interactions between multiple characters, which elicits the disruption of the white imaginary by Black individuals. As the show progresses and continues to release seasons that reach such large audiences, it is crucial to analyze what discourses surrounding race are being perpetuated and its potential impact on reinforcing stereotypes and misrepresenting marginalized communities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.231
Teacher spread0.183 · 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 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
Published2024
Admission routes1
Has abstractyes

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