Bibliographic record
Abstract
For the first time in human history, over half of the world's population lives in cities.Despite its geographic size and global reputation for pristine natural environments, Canada is an especially urban nation.Upwards of 80 per cent of Canadians live in cities, with more than one-third residing in just three urban agglomerations -Toronto, Montreal, and Vancouver.Canada is a country that has steadily urbanized since confederation, a time when over four out of every five Canadians lived in rural areas.Urbanization took off after the Second World War and largely stabilized by the 1970s. 1 Canada's centennial thus took place in a human geographic landscape that was in the midst of a dramatic transformation.The rapidity of that transformation outpaced the policy and fiscal capacity to respond, and we are living with that legacy at the sesquicentennial.It is important that Canadians consider the complex relationship between cities and the natural environment.Cities are critical engines of economic growth, production, innovation, culture, and prosperity.Yet the activities that take place in cities and make them vibrant and prosperous -such as hypermobility, density, and commercial and industrial activity -are energy-intensive and in many cases highly polluting.According to UN Habitat, nearly 80 per cent of all energy resources globally are consumed in cities, while cities generate over 60 per cent of the carbon dioxide emissions that contribute to climate change. 2Through the daily activities of individual residents and industrial processing, cities also consume large amounts of water and produce large amounts of solid waste.Localized air, water, waste, and soil pollution emitted in cities causes significant health hazards and premature death, with great disparities 9 The Environment as an Urban Policy Issue in Canada matti siemiatycki
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.008 |
| Scholarly communication | 0.019 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".