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Record W3214208414

Colourblind Racism Discourses in YouTube Review Videos of "Just Mercy"

2021· article· en· W3214208414 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Language Analysis
Canadian institutionsMacEwan University
Fundersnot available
KeywordsRacismWhite (mutation)SociologyRace (biology)Gender studiesMedia studies
DOInot available

Abstract

fetched live from OpenAlex

In the current age of the COVID-19 pandemic with issues about race and discrimination becoming more apparent, many individuals turn towards media to learn more about race and racism in the world. Therefore, this research project aims to explore how white audiences are discussing films that depict race-based issues. “Just Mercy”, directed by Destin Daniel Cretton, depicts the true story of civil rights defence attorney Bryan Stevenson as they work to free wrongly convicted African Americans on death row. Using critical discourse analysis, this study explores whether colourblind racism discourses are present in how white audiences discuss the film “Just Mercy”. To do so, this project will be using Eduardo Bonilla-Silva’s four frames of colourblind racism and Jayakumar and Adamian’s fifth frame of colourblind racism to analyze movie review videos published by white YouTubers. Through the analysis of these videos, the findings indicate that Jayakumar and Adamian’s fifth frame of colourblind racism is used more commonly by white individuals in racially conscious contexts than Bonilla-Silva’s initial four frames. Department: Sociology Faculty Mentor: Dr. Kalyani Thurairajah

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.

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 categoriesInsufficient 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 score0.587
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.214
GPT teacher head0.468
Teacher spread0.254 · 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