Bibliographic record
Abstract
Big things are accomplished together. Civility is the glue that binds humans together into disparate, productive groups. So much of human history is about competing for perceived and genuinely scarce resources. The future is however less about scarcity and more about traveling together within a common goal that supports the flourishing of all. Civility, most often nurtured in cities, is essential to developing a metropolitan mindset that moves past the traditional, zero-sum logic of most politics. Building on this mindset, sustainability can be extended across the globe. Recognizing that environmental, economic, or social degradation anywhere is eventually felt everywhere, this chapter concludes with several recommendations to move Canadian cities along the road toward sustainability. These include developing new approaches to housing; establishing at least five Chief Resilience (Sustainability) Officers; catalyzing a new International Mining and Materials Association; expanding the benefits of post-secondary education; enhancing data management systems; empowering Statistics Canada to provide urban sustainability indicators and data connected to local sustainable development goals; and promoting an international outlook within Canadian cities that benefits everyone.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".