Metrics of Urban Sustainability: A Case Study of Changing Downtowns in Thunder Bay, Canada
Bibliographic record
Abstract
Thunder Bay, a medium-sized city in Northern Ontario, has a twin downtown core model, arising from the merging of two former cities in 1970. Its north core, designated as the City’s Entertainment District has received considerable investment, notably a major waterfront renewal project undertaken in 2009 as part of an overall strategy towards downtown revitalization. Greater diversity of commercial functions and increasing residential capacity in downtowns are considered positive steps towards sustainable urban development. It is hoped the leadership taken by the City in its downtown capital investments can stimulate others (corporations and individuals) to re-invest in both living and working in more central locations to the benefit of environmental sustainability indicators like journey-to-work (distance and mode selected) and residential density. This article tracks changes in business composition and residential capacity during a five year period via the development of an intensive database of business and institutional activities. Urban sustainability metrics developed include residential capacity and density, business vacancy rates and business composition and turnover, which complement an existing measure of land-use diversity developed in earlier research. While major capital investments in downtown revitalization (such as the waterfront project) have fairly long-term impact horizons, data suggest some positive trends in the developed metrics in the downtown north core since 2009. In particular, there have been notable investments in waterfront condos and downtown lofts and some diversification in the food retailing and restaurant sectors. However, overall trends in downtown commerce are currently flat, indicative of a struggling local economy and a continued suburbanization of key commercial sectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".