Bibliographic record
Abstract
Abstract: The last five years have witnessed a series of books exploring the post–World War II rise and growth of the American West and South, an area of the United States sometimes called the Sunbelt. The term, coined in1969 by Kevin Phillips, identified a rather amorphous region that was undergoing rapid transformation and was, therefore, vital for long-term Republican electoral success. However, scholars do not universally accept the “Sunbelt Phenomenon,” nor has the concept produced intellectual consensus. Instead, authors are now rethinking previous methodologies about the United States after World War II and reformulating the configuration of social, political, and economic categories of analysis. The Sunbelt concept is now being renegotiated. In recent years, works by Michelle Nickerson and Darren Dochuk; Jeff Roche; Elizabeth Tandy Shermer; and Kim Phillips-Fein and Julian Zelizer have signalled the energy of the debates as well as the evolving literature as a whole. Problematically, as much as this new literature shows expansion, the scholarship also demonstrates a small, though consequential, silo mentality.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".