Bibliographic record
Abstract
Why Americans do not divide neatly into red and blue or right and left but form coalitions across party lines on hot-button issues ranging from immigration to same-sex marriage.On any given night cable TV news will tell us how polarized American politics is: Republicans are from Mars, Democrats are from Canada. But in fact, writes Peter Wenz in Beyond Red and Blue, Americans do not divide neatly into two ideological camps of red/blue, Republican/Democrat, right/left. In real life, as Wenz shows, different ideologies can converge on certain issues; people from the right and left can support the same policy for different reasons. Thus, for example, libertarian-leaning Republicans can oppose the Patriot Act's encroachment on personal freedom and social conservatives can support gay marriage on the grounds that it strengthens the institution of marriage.Wenz maps out twelve political philosophies—ranging from theocracy and free-market conservatism to feminism and cosmopolitanism—on which Americans draw when taking political positions. He then turns his focus to some of America's most controversial issues and shows how ideologically diverse coalitions can emerge on such hot-button topics as extending life by artificial means, the war on drugs, the war on terrorism, affirmative action, abortion, same-sex marriage, health care, immigration, and globalization.Awareness of these twelve political philosophies, Wenz argues, can help activists enlist allies, citizens better understand politics and elections, and all of us define our own political identities.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.141 | 0.044 |
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".