Trading off sustainable development in Canadian cities: theoretical implications of SDG 11 indicator aggregation approaches
Bibliographic record
Abstract
Sustainable Urban Development requires an optimization of multi-dimensional targets across social, economic, and environmental pillars of development. These multi-dimensional targets are largely captured by the United Nations Sustainable Development Goals, which comprise 17 goals spread across pillars of sustainable development. The pursuit of these targets, however, often exposes synergies and trade-offs between the goals. Broader discussions of trade-offs between human and natural capital have been conceptualized along the contours of weak versus strong conceptualizations of sustainable development. This challenge is exposed not only in strategizing sustainable urban development but also in measuring progress toward that aim. With this background in mind, there is limited research to indicate how Canadian cities are progressing toward the achievement of the Sustainable Development Goals and the extent to which trade-offs in SDG performance should be treated. This investigation collected indicators for SDG 11, Sustainable Cities and Communities, on 18 Census Metropolitan Areas in Canada for the purpose of designing an index of SDG achievement. The resulting index aggregation measures compared performance depending on whether the CMAs were allowed to trade-off performance across the SDG 11 indicators. The results expose the significant role of non-compensatory aggregation methods (which do not allow the trade-off of performance) when measuring sustainable development. The implications of these findings demonstrate the need to consider policy pathways that address these trade-offs and consider how that progress is measured.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".