Bibliographic record
Abstract
had 624,464 residents. The county's population is projected to increase by 20.4 percent by the year 2008. In 1997, Mecklenburg County per capita personal income was $32,295, higher than any other county in North Carolina. Per capita income for the state as a whole was $20,217. The median family income in the county was $61, 397. In 1990, 81.6 percent of county residents had a high school degree and 28.3 percent had a college degree. In 1998, 347,740 residents, nearly 56 percent of the county's population, were in the labor force. According to the EDIS County Profile, during the first quarter of 1999, the service industry employed the largest number of workers--130,209 workers, or 27.1 percent of the labor market. Retail trade employed 78,557 people or 16.4 percent of the county's labor force. Finance, Insurance and Real Estate make up the third largest segment of the workforce with 11.5 percent or 55,276 people. Charlotte is one of the top banking cities in the United States. The fourth largest employment area is government sector jobs with 50,677 or 10.6 percent of the labor force in this occupation. "Mecklenburg County is primarily a banking and technology community which requires a more highly skilled workforce than what might be found in a rural area. The special challenge for the Work First program will be supporting the development of skills to meet
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.872 | 0.839 |
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".