Institutions and sustainable transport : regulatory reform in advanced economies
Bibliographic record
Abstract
Contents: 1. Institutions and Regulatory Reform in Transport: An Introduction Piet Rietveld and Roger R. Stough PART I: BARRIERS TO IMPLEMENTATION OF TRANSPORT 2. Making Sustainable Transport Politically and Publicly Acceptable: Lessons from the EU, US and Canada David Banister, John Purcher and Martin Lee-Gosselin 3. Equity and Environmental Justice in Sustainable Transportation: Toward a Research Agenda Elizabeth Deakin 4. Successes and Failures in Innovations Towards Sustainable Transport J.C.J.M. van den Bergh, E.S. van Leeuwen, F.H. Oosterhuis, P. Rietveld and E.T. Verhoef 5. A Comparative Analysis of US and European Approaches to Dealing with Uncertainty: The Case of the Human-Machine Interface (HMI) in Advanced Driver Assistance Systems (ADAS) Jonathan L. Gifford and Vincent Marchau PART II: REGULATORY REFORM 6. Rail Reform in Europe: Issues and Research Needs Chris Nash and Cesar Rivera-Trujillo 7. US Railroad Productivity and Deregulation: A Brief Summary of Findings John D. Bitzan 8. Airlines: Sustainable Development in a Transatlantic Context Kenneth Button 9. Integration: An Instrument for Sustainability of Urban Mobility Systems Rosario Macario 10. Transport Pricing when Several Governments Compete for Transport Tax Revenue Bruno De Borger and Stef Proost PART III: PUBLIC-PRIVATE COOPERATION 11. Public and Private Roles in Transport Network Development Steve Lockwood 12. Private Sector Finance of Transport Infrastructure: Progress and Prospects Roger Vickerman 13. A Framework for Assessing Public Private Partnerships David Levinson, Reinaldo C. Garcia and Kathy Carlson Index
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".