Bibliographic record
Abstract
Americans are far more divided than other Westerners over basic issues, including wealth inequality, health care, climate change, evolution, the literal truth of the Bible, apocalyptical prophecies, gender roles, abortion, gay rights, sexual education, gun control, mass incarceration, the death penalty, torture, human rights, and war. The intense polarization of U.S. conservatives and liberals has become a key dimension of American exceptionalism—an idea widely misunderstood as American superiority. It is rather what makes America an exception , for better or worse. While exceptionalism once was largely 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 and injustices. They also shed light on the peculiar ideological evolution of American conservatism, which long predated Trumpism. Anti-intellectualism, conspiracy-mongering, radical anti-governmentalism, and Christian fundamentalism are far more common in America than Europe, Canada, Australia, and New Zealand. Drawing inspiration from Alexis de Tocqueville, Mugambi Jouet explores American exceptionalism’s intriguing roots as a multicultural outsider-insider. Raised in Paris by a French mother and Kenyan father, he then lived throughout America, from the Bible Belt to New York, California, and beyond. His articles have notably been featured in The New Republic , Slate , The San Francisco Chronicle , The Huffington Post , and Le Monde . He teaches at Stanford Law School.
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 | 0.000 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.188 | 0.045 |
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".