Bibliographic record
Abstract
Although Twin Cities metropolitan area has a reputation for good planning, its transit system is very limited. This article explains how this situation arose, and discusses how transit is beginning to grow in the region. In the 1950s, an extensive street railway system was demolished and freeways were built on a giant grid. Since many suburban residents had roots in rural areas, they regarded suburbs as independent places rather than as part of an interconnected metropolitan area. Political factors such as a reluctance to raise taxes and intercity arguments between Minneapolis and St. Paul regarding the potential rail line location, also doomed any efforts to overturn a 1985 legislative ban on rail transit in the state. However, growth in the downtown residential population is helping spur investment in transit. In 2008, a transportation bill was passed that included a sales tax hike to expand the area's lone light rail line into a full-scale transitway system with rail and rapid bus operations. The first commuter rail line will open in 2009. However, challenges remain. The cities will have to be transformed from their current automobile-focused regimes into a more integrated, efficient and balanced metropolis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.091 | 0.028 |
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".