Recruiting and Retaining Individuals in State Transportation Agencies
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
This report will be of interest to state departments of transportation (DOTs) management and personnel, as well as to other professionals in both the public and private sectors, who deal with the issue of recruitment and retention at the professional level. Work-force issues are at the forefront of discussions occurring within the ranks of public agencies and throughout corporate America. This synthesis contains information culled from survey responses from transportation agencies and selected state employees. Surveys were sent to the 50 states and affiliate members of the American Association of State Highway and Transportation Officials, and 13 Canadian provinces to assess the various strategies currently in practice, as well as gather data about a variety of agency characteristics. A second survey of state employees in Maryland, Nebraska, and Utah was undertaken in an attempt to validate, in both utility and effectiveness, the strategies identified by the states. This information is combined with and reviews applicable literature to yield a compendium of successful practice, including those that might have the greatest potential for success and implementation in other state and province DOTs.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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