Squatter Regionalism: Postwar Fiction, Geography, and the Program Era
Bibliographic record
Abstract
In this article we use computational methods to establish that the Program Era has altered the traditional understanding that a regionalist writer writes about the region in which they grew up. Using the Iowa Writers’ Workshop as an example, we prove that many writers now write about the region to which they moved to study and/or teach creative writing. Using a database of demographic information about faculty and students alongside computational analysis of place names in a curated corpus of work produced by prominent Iowa-affiliated writers, we map authorial career itineraries onto the geographic locations referenced in their fiction, visualizing the ways in which the relationship between writer and place has been inflected by the Midwestern location of the Workshop. We found that Iowa references are significantly higher than in a comparable corpus of postwar literature. They are also significantly higher in percentage terms than Iowa’s population as a proportion of the US population. Finally, we found that the works in our corpus most centrally focused on Iowa are, overwhelmingly, not authored by Iowa natives. Instead, we have identified a cohort of squatter regionalists, authors whose writings prominently feature the state in which they received their MFA, found faculty employment, or (frequently) both. This trend, we believe, may also be evident in works by authors from other MFA programs, which would confirm our larger hypothesis that the professional itineraries mandated by the Program Era have influenced the regional settings of postwar American fiction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".