Tax-free employer-provided benefits for public transport: background, history and 2008 status
Bibliographic record
Abstract
Information is provided on the history and the use of employer-focused (payroll-based tax incentives) in the USA since first being introduced in the 1970s, through changes made in 2008. In the US, transit benefits allow tax savings of over 40% for most employees, and 8% for employers. The underlying concepts and various applications of transit benefits will be presented, including programs implemented by local public transport agencies and those offered by private services. Information on the documented impacts of transit benefit programs on public transport ridership in various cities will be reviewed. Explanations of the larger-than-expected success of the initiatives will be provided, along with the central elements required for successful programs. The evolution of practices and current use of technology in transit benefit programs will also be discussed. Current influences on transit benefit practices will be noted, and newdirections will be summarized. The value of transit-supportive tax policies as a means of drawing the business community into traffic reduction activities will be discussed. Status reports on current efforts to expand thelegislation/regulations in the US, Canada and the UK will be provided. (The UK Program began in autumn 2007, and Canada's legislation is expected to be established in mid-2008.). For the covering abstract see ITRD E145999
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".