Hampshire County Labor Market Summary
Bibliographic record
Abstract
This summary provides an overview of the current performance of the Hampshire County labor market compared to the Winchester metropolitan statistical area (MSA), Potomac Highlands Region, the state of West Virginia, and the U.S. Table 1 below contains summary data for the county, the region, metropolitan areas with component counties in the region, the state, and the nation.The Potomac Highlands Region includes Grant, Hampshire, Hardy, Mineral, and Pendleton counties and the newly designated Winchester MSA includes Hampshire County in West Virginia and Frederick County and Winchester City in Virginia.Data summarized in this document (and detailed in the associated Excel database) include job growth and wages by industry, the unemployment rate and labor force participation, population growth and demographics, educational attainment, job turnover by industry, farming, and commuting patterns.Hampshire County added 251 jobs during the 2001-2004 period, which translates into an average annual growth rate of 2.1 percent per year.The county's job growth during the period beat the Potomac Highlands region (-0.3 percent), state (0.0 percent) and national (-0.1 percent) rates.As Figure 1 shows, the county added jobs at a rapid clip in construction; professional and business services, while also posting solid gains in trade, transportation, and utilities; financial activities; education and health care; government; and leisure and hospitality.Strong job gains in construction reflect strong population growth in the county.The county posted job losses in natural resources and mining; manufacturing; information; and other services.-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 Total
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.169 | 0.052 |
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".