Bibliographic record
Abstract
Why did Donald Trump follow Barack Obama into the White House? Why is America so polarized? And how does American exceptionalism explain these social changes? In this provocative book, Mugambi Jouet describes why Americans are far more divided than other Westerners over basic issues, including wealth inequality, health care, climate change, evolution, gender roles, abortion, gay rights, sex, gun control, mass incarceration, the death penalty, torture, human rights, and war. Raised in Paris by a French mother and Kenyan father, Jouet then lived in the Bible Belt, Manhattan, and beyond. Drawing inspiration from Alexis de Tocqueville, he wields his multicultural sensibility to parse how the intense polarization of U.S. conservatives and liberals has become a key dimension of American exceptionalism—an idea widely misunderstood as American superiority. While exceptionalism once was a source of strength, it may now spell decline, as unique features of U.S. history, politics, law, culture, religion, and race relations foster grave conflicts. They also shed light on the intriguing ideological evolution of American conservatism, which long predated Trumpism. Anti-intellectualism, conspiracy-mongering, a visceral suspicion of government, and Christian fundamentalism are far more common in America than the rest of the Western world—Europe, Canada, Australia, and New Zealand. Exceptional America dissects the American soul, in all of its peculiar, clashing, and striking manifestations.
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 | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.023 |
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".