Bus drivers take the learning‐center route to better basic skills
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how First Bus UK is improving the basic skills of its employees through learning centers. Design/methodology/approach Describes how First Bus UK teamed up with the Transport and General Workers' Union to establish 40 learning centers across the UK, and looks in detail at the operation of one such center, in Orpington, Kent. Findings Reveals that, since the centers were set up five years ago, more than half the company's 20,300 drivers have undergone training, ranging from union learner‐representative courses at levels one to three to the European Computer Driving Licence (ECDL), from literacy and numeracy to computing basics, and from English as a second language to road safety. Last year, more than 5,000 drivers achieved NVQ level two, 74 completed the learning‐representative course and 17 gained the ECDL. Practical implications Argues that the centers are delivering significant benefits to the business, employees and the wider community. Originality/value Shows that the training has benefited drivers and the company because: the ability to read and follow safety instructions has led to a reduction in staff injuries and lost‐time accidents; there has been an 11 percent drop in physical assaults on staff; driver turnover has fallen, saving a seven‐figure sum; and improved driver availability has led to the lowest “lost mileage” level for two years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".