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 pétrole et le gaz non conventionnels peuvent réduire la pénalité pour la mortalité rurale? Une étude des comtés américains Résumé Aux États-Unis, les zones rurales sont de plus en plus confrontées à des problèmes apparemment insolubles - le plus grave d'entre eux est peut-être la « peine de mortalité rurale » dans laquelle les zones rurales ont des taux de mortalité plus élevés que les zones suburbaines et urbaines. L'essor de la production de pétrole et de gaz non conventionnel au milieu des années 2000 a apporté avec lui la promesse d'un renouveau rural et le potentiel de relever les défis de développement de longue date de l'Amérique rurale. Dans cette analyse, nous nous demandons comment le boom pétrolier et gazier a eu un impact sur la peine de mortalité rurale. Nos résultats impliquent que le développement du pétrole et du gaz n'améliorera ni n'endommagera les taux de mortalité. Les implications pour les populations rurales sont discutées. Mots-clés: Peine de mortalité rurale; développement pétrolier et gazier; modèle à plusieurs niveaux
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 | 0.001 | 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".