Reviewing the performance of the Australian land transport sector against its counterparts in Canada, New Zealand and the USA
Bibliographic record
Abstract
This paper compares the longitudinal performance of the land transport sectors in Australia, Canada, New Zealand and the United States of America (USA). It comprises analysis of freight transport, passenger transport, energy consumption, CO2 emissions, road fatalities and gross direct investment. Among its key findings are that annual growth in overall freight transport volumes has been recently increasing overall at a faster annual rate than in Canada, New Zealand and the USA, but has slightly lower than average annual compound growth in GDP. Annual growth in total passenger transport by road and rail on the other hand has been less than one quarter of the average compound rate of growth in GDP over the years 1990-2007. Private motor vehicles are still the predominant mode for passenger transport across the four nations, but Australia has the lowest modal share in comparison to Canada and the USA. The transport sector between 1997 and 2007 has performed relatively well in respect of the percentage increase in final energy consumption. However more needs to be done or at least attempted to reduce or at least keep steady the transport sector's role on CO2 emissions. Finally Australia's transport sector is achieving average annual percentage reductions in road fatalities that are larger than those in any of the three comparator nations. Based on these findings some broad policy implications are briefly outlined. The paper finally identifies some limitations of this research and proposed areas for future research
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".