Can Road User Charging Be Made Publicly Acceptable? Lessons Learnt – Why Did the Manchester Congestion Charging TIF Submission Fail to Win Public Support
Bibliographic record
Abstract
For those of us who watch the world of road pricing and tolls, the overwhelming “no” vote against the Transport Innovation Fund (TIF) package in Manchester, UK is a significant set back for road pricing in the UK and perhaps the world. Opponents of the concept that pricing can manage demand, despite proven success in other markets and for utilities, will point to the Manchester results and say that people may accept it in other markets but transport is different, drivers are different or already pay enough. Road Pricing is just another tax on top of several other taxes and fees. “Enough is enough” is the emotive cry from this quarter. Others content that the benefits were not clear. Still others state that there was no “trust” in government to deliver the “package” of benefits. Many put the fault on political differences or the lack of a clear leader or “champion”. Some are more philosophical and point to post-ballot sentiments that road user charging is inevitable, but in a recessionary economic period people will not vote for any new charges or taxes.
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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 | 0.030 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".