2018 Quarter 3: Southwest VA Workforce Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.100 |
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 it