2018 Quarter 3: Southwest VA Workforce Report
Bibliographic record
Abstract
Welcome to the 2018 quarter three workforce report, produced by the Virginia Tech Office of Economic Development. This document focuses on gender in the workplace at the national and regional level. National trends provide context for gender-based wage differences and the barriers women face throughout their time in the workforce. Regional trends illustrate how these differences affect the seven counties and city that comprise the workforce area. This report begins by outlining national trends related to gender -based workforce inequalities and details information on the role of gender in labor force participation, highlighting disparities between education attainment and career opportunities for men and women. The report continues this focus on page four, displaying data on female representation at all levels of the corporate ladder as well as information related to female representation and weekly wages for national sectors and female employment in science, technology, engineering, and mathematics (STEM) fields. This quarter’s data snapshot then focuses on regional trends, including information and data related to demographic changes, female labor force participation, and female representation in regional industry sectors. Page six offers an overview of occupations for both men and women. Additionally, a map illustrating female representation in the regional labor force and the gender wage gap is included on page six. The next two pages (seven and eight) Include information and data related to female employment in GO Virginia target industries within the region as well as other industries important to the area economy. Page nine includes brief summaries of interviews with women working in some of the region’s target industries. These interview summaries offer personal experiences and perspectives from females in industries or occupations where women may be underrepresented. The report concludes with a brief summary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.137 | 0.116 |
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".