2017 Quarter 4 New River Mount Rogers (WDA II) Quarterly Workforce Report
Bibliographic record
Abstract
Welcome to the 2017 fourth quarter workforce report, produced by the Virginia Tech Office of Economic Development on behalf of the New River Mount Rogers Workforce Development Board (WDA II). This region comprises the New River Valley Regional Commission and the Mount Rogers Planning District, including the counties of Bland, Carroll, Floyd, Giles, Grayson, Montgomery, Pulaski, Smyth, Washington, Wythe and the independent cities of Bristol, Galax and Radford. These quarterly reports present an analysis of regional labor supply and demand data, to inform the public of the evolving workforce landscape. Please note that the data presented in this report is the most up-to-date data available as of December 2017. In this quarter’s report, we explore some basic regional trends including population, employment, and workforce gaps. We then delve into four industries targeted by two regional GO Virginia Growth and Diversification Plans. WDA II straddles GOVA regions one and two. Region I is comprised of Workforce Development Area I and the Mount Rogers Planning District—spanning from Washington County in the southwest, Carroll County in the southeast, Buchanan County in the northwest, and Bland County in the northeast. Region 2 comprises the New River Valley and Workforce Development Area III (Roanoke)—spanning from Pulaski in the southwest, Franklin County in the southeast, Giles in the northwest, and Alleghany County in the northeast. GO Virginia is a recent initiative approved by Virginia’s House of Delegates, which offers funding for suitable workforce and economic development projects. By improving collaboration across regions, this initiative is meant to leverage local capacity to encourage the development of targeted industry clusters that offer predominantly higher wages and attract money from outside the state. GO Virginia regions across the state have identified four or more target industry clusters, and have developed strategies to grow and diversify these clusters. Arguably one of the most prominent strategies identified by all GO Virginia regions was to growth, retain, and attract skilled workers. This report will provide some basic data on the target GOVA clusters in WDA II and highlight assets and areas where WDB stakeholders may play a role in GO Virginia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.096 |
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".