Bibliographic record
Abstract
The modern streetcar is making a comeback throughout North America as an attractive mode of transit with claims to both reduce congestion and shape land-use. As the City of Vancouver plans for a streetcar in the downtown core, this professional project sets out to objectively inform the public of factors that necessitate consideration when pursuing this type of urban rail system. A discussion of the project proposal, related literature, and local context together provide a perspective of what these considerations are for the City of Vancouver. This report was formulated after an examination of research which reveals ridership levels and capital costs being discrepant to initial estimations in light rail systems currently operating. Literature also reveals that the development and land-use benefits that attract cities to pursue fixed rail systems are attributed more directly to the land-use measures that support them than the transit system itself. Streetcar infrastructure continues to be pursued, however, in dozens of North American cities for its ability to attract patrons, catalyze development and direct urban growth. Because a streetcar system is a significant public investment, the potential benefits must be weighed against the high costs. Most importantly, the citizens of Vancouver should understand the risks as well as the benefits accrued from the proposed system within a broader transportation context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".