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Record W7038309467

Hampshire County Labor Market Summary-Update 2006

2006· article· en· W7038309467 on OpenAlexaboutno aff

Bibliographic record

VenueThe Research Repository @ WVU (West Virginia University) · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaWest virginiaUnemploymentPopulationCurrent Population SurveyCensusQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

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 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 281 jobs during the 2001-2005 period, with 30 of those jobs coming last year.Job gains since 2001 translate into an average annual growth rate of 1.8 percent per year.The county's job growth during the period beat the Potomac Highlands region (0.3 percent), West Virginia (0.3 percent), and national (0.4 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; and other services, while registering little change in information.-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.025
GPT teacher head0.221
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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