Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.166 | 0.029 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".