2019 Quarterly Workforce Report 1: New River/Mount Rogers WDA II
Bibliographic record
Abstract
Welcome to the 2019 quarter one workforce report. The Virginia Tech Office of Economic Development produced this document on behalf of the New River/Mount Rogers Workforce Development Board. This workforce report details unemployment in Bland, Carroll, Floyd, Giles, Grayson, Montgomery, Pulaski, Smyth, Washington, Wythe counties and the cities of Bristol, Galax, and Radford from 2008 through 2018. The unemployment rate counts only those who have actively looked for work in the past month but are not working. Lower unemployment often correlates with other community health indicators such as higher standards of living, greater mental and physical health, higher educational attainment, and lower crime rates. Yet, as our country and regions begin to reach such low levels of unemployment that media and policy makers begin talking about the country reaching “full employment,” how useful does the unemployment rate become in assessing the overall health of a region? The goal of this report is to gain a more accurate understanding of unemployment in the region and explore populations that may not even be counted in the unemployment number because they left or never entered the labor force. These populations could serve as untapped human capital, ready for employment or better employment in the region’s industries if given the appropriate training, tools, and connections with amenable employers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.161 | 0.129 |
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".