Here Today: Oklahoma’s Ghost Towns, Vanishing Towns, and Towns Persisting Against the Odds. By Jeffery B. Schmidt
Bibliographic record
Abstract
Schmidt, a professor of Marketing and Supply Chain Management at the University of Oklahoma, is more freewheeling observer than academic historian in his survey of Oklahoma ghost towns and vanishing towns, but he does grapple with a complex question: what leads a vibrant community into a spiral of decline? In a short literature review of other works on the subject, it becomes clear that population decline is the common denominator, but a place need not be devoid of residents to make Schmidt’s list. In fact, some of the towns in the book are enjoying a decent afterlife as tourist destinations. (Have a world-famous hamburger in Meers, Oklahoma, population 0.) This is not the first book on the subject, but John W. Morris’s 1978 book, Ghost Towns of Oklahoma, neglected one of the most compelling categories of abandoned communities in the state, all-Black towns, of which there were once at least fifty. Only eleven such towns remain, leaving ample room for Schmidt to explore how and why such vaunted experiments in African American self-determination declined. The story of Taft (pop. 174) is telling in this regard. Taft businessman William Henry Twine sued a railway for imposing segregation on passenger cars servicing the town, and a depot built with segregated waiting rooms was burned down. Twine feared assassination by White supremacists in nearby Muskogee. The first Black woman elected mayor in the United States occurred in Taft, and a newfound enthusiasm for Black cowboy culture will find much to admire in vanishing towns like Taft, Red Bird, and Clearview.
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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 | 0.021 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".