The Effective Use of Computer-Based Training for In-Career Training and Knowledge Management
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 paper describes how the theme for the 2006 Transportation Association of Canada (TAC) Annual Conference, “Transportation without Boundaries” creates an image in the minds of most transportation professionals of what the world would like to achieve within the next decade. However, the pragmatic side says there are institutional barriers that need to be breached and entrenched work cultures that must be changed in order to attain such a vision. A computer-based training (CBT) program is helping transportation agencies in Canada and the United States (US) transcend traditional training boundaries of time and distance to improve winter maintenance operations. The CBT is being used in both individual and conventional group training modes. It is most effective and efficient as an individual self-paced instruction program. Both the group and individual modes are structured to serve a wide range of learning abilities in the student population, including those with challenges such as dyslexia or hearing loss. In an effort to reach field forces more effectively, the American Association of State Highway and Transportation Officials (AASHTO) launched an Anti-Icing/Road Weather Information System (AI/RWIS) CBT into the winter maintenance training arena. Over 90% of US snow-belt states, three provinces and numerous cities in both countries have purchased the CBT. This paper describes how the AI/RWIS CBT is being utilized in both countries and underscores the valuable lessons learned from successful and not-so-successful deployments. It also shows how institutional barriers were breached, entrenched work cultures changed, and how recent research is being incorporated into the instructional content to ensure that training material stays current.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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