An Analysis of Public Debates over Urban Growth Patterns in the City of London, Ontario
Bibliographic record
Abstract
In much of the developed world, the direction and patterns of urban growth have been the subject of public debate. Some scholars and practitioners believe that the current urban development pattern are too outward-oriented and are concerned about its possible negative consequences. Others defend outward expansion, arguing that it fulfils consumer preferences and promotes economic growth. Despite a sizeable literature on the topic, the discussion has been hampered by a lack of knowledge about how growth is perceived by key “agents of change”, those individuals whose decisions and activities affect the direction and patterns of urban growth. Additionally, the news media often represents the urban growth debate in simplistic, oppositional terms (e.g. “pro-growth” versus “anti-growth”, “pro-business” versus “anti-environment”) with little or no regard for local factors that affect development patterns in specific situations. As described in this article, we used a multi-method case study approach to address these limitations and to better understand recent urban growth issues. The primary goal of this study is to assess multiple perceptions of urban growth and management debate in London, Ontario. As elsewhere, the issue of urban growth patterns is intensely debated in this case study. However, we argue that discussion on this topic should change from simplistic generalisations to consideration of locale-specific factors that influence urban growth patterns.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".