Exploring the Potential for a Holistic Indicator of Social Sustainability and Quality of Life in Vancouver
Bibliographic record
Abstract
This report investigates the feasibility of developing a holistic indicator to assess and communicate the social sustainability and quality of life in Vancouver. Despite the availability of various specific metrics, there is a noted absence of a comprehensive framework that integrates these metrics to provide a singular, actionable view of the city's progress towards its social sustainability goals. The City of Vancouver currently employs 45 population-level indicators under its Healthy City Strategy, demonstrating the city's commitment to transparent and data-driven governance. However, these indicators, while effective individually, do not collectively provide a complete picture of the city's overallhealth across various dimensions such as public health, housing, education, and environmental sustainability. The aim of this research was to identify a holistic indicator that encompasses multiple dimensions of social sustainability to simplify assessments and improve strategic planning. Through a desktop review of 70 existing indicators and consultations with experts, two models were identified as particularly promising: the Greater London Authority's (GLA) Wellbeing and Sustainability Measure, and the City of Calgary's Equity Index (CEI). These models offer robust frameworks that prioritize equity, accessibility, and stakeholder involvement, aligning closely with Vancouver's urban development goals. This executive summary highlights the need for an overarching metric that reflects the interdependencies among various domains, ensuring that progress in one area does not undermine another. By leveraging insights from this research, Vancouver can enhance its policy implementation and community engagement, moving closer to achieving a balanced and sustainable urban environment. The proposed holistic indicator will also support the city in benchmarking against other urban centers and refining its strategic initiatives based on quantifiable metrics.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".