Dominant Racial Discourses in Social Media Through the Lens of Black Representation in Film
Bibliographic record
Abstract
The topic for this research project involves the observation of racial discourses in social media; in particular, this project will be examining how discourses surrounding the Black Lives Matter movement are reflected in Tweets made in response to the casting announcement for actress Yara Shahidi’s involvement in Disney’s upcoming Peter Pan and Wendy film. This project will focus on Tweets made with the hashtags #BlackLivesMatter and #BLM, as well as Tweets responding to Yara Shahidi’s casting announcement, with both categories of Tweets being pulled from a time frame of September 20, 2020, to October 05, 2020. This time frame was chosen based on the time when Shahidi’s casting was announced, on September 25, 2020, in order to observe the discourses on the Black Lives Matter movement that were taking place before, during, and after the announcement. This project is being conducted with the hopes of demonstrating how opinions on Black actors’ casting in traditionally white roles demonstrate the overarching discourses which are present in online conversations surrounding the Black Lives Matter movement. Department: Sociology Faculty Mentor: Dr. Kalyani Thurairajah
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".