Does Design Matter? The Ecological Footprint as a Planning Tool at the Local Level
Bibliographic record
Abstract
This paper provides a comparative environmental analysis of three subdivision designs for the same site: an ecovillage, a new-urbanist design and an up-scale estate subdivision. The comparison is based on ecological footprints (EF). Based on built form alone, the higher-density subdivisions resulted in lower EF. Consumption data were limited to the ecovillage, since this is the actual use of the study site, but comparisons were made with regional US averages. The study suggests that consumption contributes more to the overall footprint than built form. Qualitative information was used to explore how consumption is influenced by urban design and self-selection. Despite the challenges associated with data collection and conversion, it is argued that EF has utility for planners and urban designers because it enables assessment of built form from an environmental consumption point of view. The problem of the 21st century is how to live good and just lives within limits, in harmony with the earth and each other. Great cities can rise out of cruelty, deviousness, and a refusal to be bounded. Liveable cities can only be sustained out of humility, compassion, and acceptance of the concept of enough. (Donella Meadows, as cited in Beatley & Manning, 1997 Beatley, T. and Manning, K. 1997. The Ecology of Place: Planning for Environment, Economy, and Community, Island Press: Washington, DC. [Google Scholar], p. 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".