Partnerships for Smart City Retrofits: The Case of Toronto’s Quayside
Bibliographic record
Abstract
<p>[introduction]: "This chapter focuses on the governance of city change including the integration of high technology into city systems, illustrated with a case study at Toronto’s Harbourfront. Many agree that it is at the city level, rather than at regional or national levels, where we will see the greatest progress on climate change mitigation and adaptation, for a variety of reasons (Hughes <a href="https://link.springer.com/referenceworkentry/10.1007/978-3-319-71067-9_16-1#ref-CR21" target="_blank">2017</a>). A UNEP estimate is that cities may spend approximately US$41 trillion by 2030 on new water, energy, and transportation infrastructure to accommodate growing populations (UNEP <a href="https://link.springer.com/referenceworkentry/10.1007/978-3-319-71067-9_16-1#ref-CR49" target="_blank">2013</a>). These impending investments in smart sustainable changes are in accordance with an urgent requirement to address the United Nations’ Sustainable Development Goals (UN SDGs), for example, Goal #11 Sustainable Cities and Communities and Goal #13 Climate Action. Making city systems smarter through technology enables higher levels of sustainability overall and thereby addresses most of the UN SDGs, either directly or indirectly."</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".