“I’m Not going to the f***ing White House”: Twitter Users React to Donald Trump and Megan Rapinoe
Bibliographic record
Abstract
When asked if she would go to the White House if invited, Megan Rapinoe stated, “I’m not going to the fucking White House.” The next morning, President Donald Trump posted a series of tweets in which he criticized Rapinoe’s statements. In his tweets, Trump introduced issues around race in the United States and brought forth his own notion of nationalism. The purpose of this study was to conduct an analysis of users’ tweets to determine how individuals employed Twitter to craft a narrative and discuss the ongoing Rapinoe and Trump feud within and outside the bounds of Critical Race Theory (CRT) and nationalism. An inductive analysis of 16,137 users’ tweets revealed three primary themes: a) Refuse, Refute, & Redirect Racist Rhetoric b) Stand Up vs. Know your Rights, and c) #ShutUpAndBeALeader. Based on the findings of this study, it appears that the dialogue regarding racism in the United States is quickly evolving. Instead of reciting the same refrain (i.e., racism no longer exists and systematic racism is constructed by Black people) seen in previous works, individuals in the current dataset refuted those talking points and clearly labeled the President as a racist. Additionally, though discussions of nationalism were evident in this dataset, the Stand Up vs. Know Your Rights theme was on the periphery in comparison to discussions of race. Perhaps, this indicates that some have grown tired of Trump utilizing nationalism as a means to stoke racism.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".