The Plantation System and the Roots of the Southern Rural Mortality Penalty.
Bibliographic record
Abstract
In recent decades, public health researchers have observed that the health of rural people has declined relative to the health of urban people in the United States. This disparity in health and life expectancy across the rural/urban divide has been described as the Rural Mortality Penalty. However, public health researchers have also noted that health and life expectancies are not uniform across the rural United States, but vary according to race, sex, gender, and other factors. Rural health disparities also vary geospatially and are especially pronounced in the American South, leading to recent calls for greater attention to the structural factors that shape the health of rural Southerners. In this study, we take an anthropological and historically explicit approach to study the impacts of systemic violence on rural health. Specifically, we focus on farm labor within the plantation system as a context where geospatial, racial, and sexual differences in mortality, often studied in isolation, find a common historical source. Here we analyze vital records data from the post-emancipation period in the Blackland Prairies ecoregion of Texas, a period when emerging forms of plantation labor such as tenant farming, convict leasing, and migrant labor were being developed to maintain the plantation economy after the abolishment of chattel slavery. We find that the plantation system remains a strong predictor of differential mortalities in rural Texas, accounting for nearly all the variance that exists across the rural/urban divide and elucidating the complex interactions of race, sex, labor, and health in the rural South.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".