Methods of Increasing Transportation Funding
Bibliographic record
Abstract
This paper describes how virtually every transportation department is facing a funding shortfall in today’s marketplace. It describes how person travel and goods movement continues to grow at a rate far faster than the ability to fund transportation infrastructure improvements. Transportation needs are outstripping available funding virtually everywhere in the world. This paper also describes how a state or other public agency could greatly increase funding available for transportation by using a range of conventional and creative but realistic approaches to funding. All methods are easily understood. The paper suggests how to tie payments to users and beneficiaries to ease public acceptance. The methods covered include a combination of user fees, user taxes, and other taxes. Some would be generated at the state level. Others could be generated at a local agency or transportation authority level and used for state transportation department projects that benefit the local area. All methods to be presented are applicable in almost all U.S. states and Canadian provinces as well as elsewhere throughout the world.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".