Making America Grate Again: The “Italianization” of American Politics and the Future of Transatlantic Relations in the Era of Donald J. Trump
Bibliographic record
Abstract
For decades the Republican Party has embracedAmerica’s open, future-oriented nationalism. But when younominate a Silvio Berlusconi you give up a piece of that.1 LATE ON THE NIGHT of Tuesday, 8 November 2016, when pundits on America’s many television networks were suddenly beginning to grasp that the all-but-guaranteed election of Hillary Clinton as the 45th president of the United States was not going to occur, a member of the team covering the day’s events for PBS offered what, in our view, was a most intriguing clue for comprehending what had happened: the American voters, remarked Jeff Greenfield, had just elected Silvio Berlusconi.2 Now, this was hardly the first time that the Republican candidate had been compared with an Italian political figure, nor would it be the last. Our purpose in this article is to reflect systematically upon this “Italianization” of American domestic politics, so curiously on display during the most recent campaign—for it is not every day, to put it mildly, that one finds such frequent appeal being made to Italian “objective correlatives” in a bid to explicate American ones.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".