The consequence of scale: process and policy implications of modelling urban sustainability using the conceptual framework of GIS‐MCDA
Bibliographic record
Abstract
We live in a world that is dominated by humans. New paradigms that blend socioeconomic development with the preservation of our natural systems are required for future generations to thrive. Conceivably, scientists know what is needed to implement sustainable growth, with extensive research completed on urban biodiversity, ecological functions and land use practices. Connections between urban processes and global conservation efforts have been made, alongside a growing acceptance of the importance of nature to the well-being of an individual. However, broad societal issues, divisive politics, and an increasingly complicated network of human and ecological systems, present significant challenges to developing and maintaining sustainable cities. Arguably, the road to success is rooted in evidence-based policy decisions that rely on data and scientific findings, since science articulates what is happening. Yet it is policy that dictates how we address it. This research investigates the theoretical foundation of multi-criteria decision analysis within geographic information systems (GIS-MCDA) as a composite indexing tool to assess relative measures of urban sustainability. This exploratory research uses scientific principles and rational frameworks to spatially structure, understand, and evaluate changing socioeconomic and environmental conditions in order to support policy and decision making practices. To accomplish this goal, a series of area-based composite indices are developed to model specific urban conditions pertaining to smart growth or sustainable planning practices for the City of Toronto, Canada. Model parameters are tested to assess index validity. Inherent uncertainties associated with modelling urban sustainability are systematically quantified and the ambiguities associated with aggregate data identified. Finally, this composite indexing approach is applied to a broader framework that links underlying social and ecological processes. The strengths and limitations of GIS-MCDA as a tool for policymakers is couched in policy and process implications throughout this research. Multi-disciplinary approaches are imperative in pursuing sustainable cities and in understanding the association between socioeconomic patterns, physical processes, and urban infrastructure. In an increasingly complex and divided society, the conceptual framework of GIS-MCDA offers a pragmatic tool that allows decision-makers to appreciate issues from a variety of perspectives, enacting novel solutions that are driven by data, science, and the public.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".