Bibliographic record
Abstract
As a generation of roadbuilders head for retirement, the industry is seeking more instruction and recruitment. In the first of a three-part series, this article takes a look at how the industry is trying to match workers for dollars, which continue to fly in following the passing of the federal surface transportation acts, whether it be through recruitment or continuing education. There are 130 courses taught through the National Highway Institute; and in 1999 alone, 557 sessions were conducted in the program throughout the country. There are also 57 Local Technical Assistance Program (LTAP) Centers located across the country. LTAPs were created to try to provide a linkage to local governments. U.S. Transportation Secretary Rodney Slater started the Garrett A. Morgan Program in 1997 to help strengthen the future of the transportation industry. The effort has already reached more than 1 million students. Two effective means of recruitment through the program have been the School-to-Work program and Job Shadow Day. The School-to-Work program focuses on mentoring, tutoring, curriculum development, career days, and work-based learning opportunities to help students become aware of and then prepare for careers in transportation. Job Shadow Day is held every February 2 and gives aspiring transportation workers the chance to come to the workplace and shadow DOT employees. Nichols Consulting Engineers of Reno, Nevada, educates thousands every year and recently started an online training program. The computer instruction, which was 2 hours once a week, included students from the United States and Canada. The Associated General Contractors of America has received strong response to their fifth-grade construction education curriculum.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.501 | 0.387 |
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".