Valuing Community Attributes in Rural Counties of West Virginia Using Data Envelopment Analysis
Bibliographic record
Abstract
As quality of life measures are related to increased economic activity, it becomes increasingly important to develop indicators as accurate measures of the well-being of the residents in a community. This study use Data Envelopment Analysis (DEA) to analyze community attributes of rural counties in West Virginia using variables determining quality of life. County level data is used to identify counties that are inefficient as measured in terms of socioeconomic factors. Desirable community attributes such as employment, median household income, median house value, health index, number of personal care establishments, and number of high school graduates were used as output variables. Input variables representing the undesirable characteristics of counties include population density, unemployment rate, per capita tax, number of persons below poverty, and crime rate. The analysis seeks to determine efficiency levels in the rural areas of the State. The results show that majority of the rural counties in the State lie on the efficiency frontier, while others are classified to be inefficient. The research findings that can be used as indicators of community performance and to evaluate counties in terms of creating quality of life are of interest to policy makers. Keywords: data envelopment analysis, community attributes, output variables, input variables, efficiency levels, quality of life, principal components analysis
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".