Bibliographic record
Abstract
(Excerpt) In Nonverts: The Making of Ex-Christian America, scholar Stephen Bullivant explores the most discussed phenomenon in American religion today: the so-called “rise of the Nones.” Nones are people without a religious affiliation; when asked on surveys to identify the religion to which they belong, they check the box that says, “no religion,” or “nothing in particular,” or “none of the above.” Starting about thirty years ago, the percentage of Nones in the United States has risen dramatically, from something like 5 percent in the University of Chicago’s well-regarded General Social Survey (GSS) in 1990 to something like 25 percent today. That’s roughly 60 million Americans. As Bullivant, a theologian and sociologist with positions at St. Mary’s University in London and the University of Notre Dame in Sydney, explains, this percentage seems likely to increase in the near term. According to the GSS, about a third of Americans below the age of 30 are Nones, though the percentage of Nones among the youngest Americans, so-called “Generation Z,” is a bit less. Although people sometimes become more religious as they age, that seems not to be happening with today’s Nones. “Barring some Great Millennial Revival,” Bullivant writes, the proportion of the religiously unaffiliated seems likely “to grow and grow for the foreseeable future.” He sets out to explain why this mass disaffiliation is occurring and what it might mean for American society.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".