Determining the Development Status of United States Counties Based on Comparative and Spatial Analyses of Multivariate Criteria Using Geographic Information Systems
Bibliographic record
Abstract
The United States ranked 8th in 2015 according to the United Nations’ Human Development Index, but empirical evidence shows that there are regions within the U.S. that would not classify as having “very high human development.” We know about domestic poverty and hardship, but there are regions in the United States that are starting to look developmentally more like Albania or Kenya. Using multivariate quantitative data (health statistics, education levels, and income) to replicate international development indices like that of United Nations on the national level, U.S. counties were ranked according to their development status. In this way, widely recognized scales of development were translationally applied to the United States to fully understand the state of development, or rather regression, in the U.S. The results were displayed cartographically to show the geographic distribution of regression across the U.S., mainly the Mississippi River Delta and the Appalachian Region. In total, there were 66 counties that fell into fourth class, or the “low development” category, for all three development criteria.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".