Settlement Patterns in the U.S. and Canada: Similarities and Differences—Policies or Preferences?
Bibliographic record
Abstract
Smart Growth advocates in the U.S. and elsewhere worry about urban sprawl and typically advocate new controls on urban growth, including tougher land use planning and regulation. Yet, is auto-oriented development the market's way of meeting widely held lifestyle preferences? Or, is it (as some critics claim) attributable to policies that favor such development? For the case of the U.S., critics suggest that policies are the problem and Smart Growth is the solution. Yet, if U.S.-type development (suburbanization and widespread auto use) can be observed in non-U.S. policy settings, the critics may really be objecting to people's preferences. Comparing recent U.S. and Canadian settlement and travel trends suggests a test. Cultural differences are minor but urban policy differences are significant. How do settlement patterns and urban transportation choices compare? Our analysis of recent data shows substantial similarities. Preferences appear to trump policies. The Smart Growth platform may have to be reconsidered. 1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".