“I’m Not going to the f***ing White House”: Twitter Users React to Donald Trump and Megan Rapinoe
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.
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.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it