Bibliographic record
Abstract
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".