Can Unconventional Oil and Gas Reduce the Rural Mortality Penalty? A Study of U.S. Counties
Bibliographic record
Abstract
Rural places in the United States increasingly face seemingly intractable problems—perhaps the most severe of these is the ‘rural mortality penalty’ wherein rural places have higher mortality rates than suburban and urban places. The boom in unconventional oil and gas production in the mid-2000s brought with it the promise of rural renewal, and the potential to address rural America’s long-standing development challenges. In this analysis, we ask how the oil and gas boom has impacted the rural mortality penalty. Our results imply that oil and gas development will not improve or damage mortality rates. Implications for rural populations are discussed. Keywords: Rural mortality penalty; oil and gas development; multilevel model --------------------------------------------------- Est-ce que le petrole et le gaz non conventionnels peuvent reduire la penalite pour la mortalite rurale? Une etude des comtes americains Resume Aux Etats-Unis, les zones rurales sont de plus en plus confrontees a des problemes apparemment insolubles - le plus grave d'entre eux est peut-etre la « peine de mortalite rurale » dans laquelle les zones rurales ont des taux de mortalite plus eleves que les zones suburbaines et urbaines. L'essor de la production de petrole et de gaz non conventionnel au milieu des annees 2000 a apporte avec lui la promesse d'un renouveau rural et le potentiel de relever les defis de developpement de longue date de l'Amerique rurale. Dans cette analyse, nous nous demandons comment le boom petrolier et gazier a eu un impact sur la peine de mortalite rurale. Nos resultats impliquent que le developpement du petrole et du gaz n'ameliorera ni n'endommagera les taux de mortalite. Les implications pour les populations rurales sont discutees. Mots-cles: Peine de mortalite rurale; developpement petrolier et gazier; modele a plusieurs niveaux
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".