Bibliographic record
Abstract
n the United States, a championship-winning sports team’s ceremonial visit to the White House has become a familiar tradition. Beginning nearly a century ago when Calvin Coolidge invited the Washington Senators to celebrate their second American League Pennant in 1924, the White House visit has evolved into such a common occurrence that we tend to take it for granted as a public relations ploy for the political elites and sports darlings of the moment, ultimately bearing little consequence for either. Yet the rhetorical strategies involved in these events reveal shifting understandings of sports’ role in American culture along with interrelated anxieties about what defines the United St ates as a nation. Especially in the context of a turbulent and divisive social climate under President Trump, the ceremonial White House visit has become an unlikely stage for displaying overarching ideological tensions. The controversies surrounding Trump’s invitations and disinvitations, as well as players’ acceptances and rejections, illuminate the inherently political dimensions of sport as athletes conform to, negotiate, or in many cases subvert societal expectations. Mounting disruptions of an enduring White House ritual have initiated a dialogue about leaders hip, honor, and freedom, foundational tenets of the US and the office that represents it.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".