The Status of Women in the Asheville Metropolitan Area, North Carolina
Bibliographic record
Abstract
and in North Carolina as a whole, have made much progress during the last few decades. The majority of women work—many in professional and managerial jobs— and women are a mainstay of the economic health of their communities. Yet, there are ways in which women’s status still lags behind men’s, and not all women are prospering equally. This fact sheet provides basic information about the status of women in the Asheville area (which includes Buncombe and Madison counties), focusing on women’s earnings and workforce participation, level of education, poverty, access to child care, and health status. It also provides background demographic information about women in the region. Basic Facts About Women in the Asheville Metropolitan Area The Asheville area is less racially and ethnically diverse than the state as a whole. Only 14 percent of women and girls in the area are from a minority racial or ethnic group, compared with 35 percent in the state overall. The proportion of women and girls in the Asheville area who are foreign-born, however, is not much lower than in North Carolina as a whole (five percent compared with seven percent; Table 1). The median age for the female population in the Asheville area is 42, four years older than in the state and the United States. Eighteen percent of women in the metropolitan area are 65 years and older, compared with 15 percent in the state and nation. As in the United States, and North Carolina overall, one in two women in the Asheville area is married.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".