Energy Service Security for Public Health Resilience: Perception and Concerns in Western Upper Peninsula of Michigan<sup>☆</sup>
Bibliographic record
Abstract
Abstract The Western Upper Peninsula of Michigan includes six rural counties and one Tribal Nation. The region is characterized by long winters, legacies of the extractive mining economy, and the infrastructural features of extreme rurality, including aging housing and low health service density. The region also faces exceptionally high electricity prices. There is limited research on the public health implications of energy service disruption in rural regions resulting from the increasing intensity and frequency of weather events caused by climate change. This article presents research findings examining the readiness of health facilities in this area to manage the rising intensity, severity, and frequency of severe weather that could disrupt energy services. The study also considers how this knowledge can guide decision‐making to improve energy service access and maintain resilient public health services in the region. This exploratory study utilized a qualitative approach that combines semi‐structured interviews with public health stakeholders and a short survey to triangulate the findings from health facilities. Given the pivotal role of dependable energy services in community health, these findings underscore the community's perception of self‐reliance as both an asset and a hurdle. This perception aligns with the realities of rural communities at the “end of the line” regarding critical infrastructure, which also serves as a formidable barrier to social organization and infrastructure access during energy service disruptions that can severely impact public health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".