Bibliographic record
Abstract
Policies promoting Toronto as a global city and provincial economic engine have been seen as beneficial to the development of all of Ontario, yet much of the province has borne significant environmental, social, economic, and political costs as a result of one city's growth. Contributors to this volume call for a radical re-imagining of public policy at local, provincial, and federal levels, that accounts for Ontario's overlooked regions. Beyond the Global City presents a kaleidoscopic view of the province - the rich fields and small towns of the southwest, the productive agricultural lands of rural Huron County, historic Kingston and the Upper St Lawrence, the social and cultural diversity of the Ottawa valley, the near mythical woodlands and waters of Muskoka and Georgian Bay, and the heavily exploited coasts and waters of the Great Lakes - to provide a deeper understanding of its various communities. In a series of regional studies, contributors describe each area's distinctive qualities and challenges and offer recommendations about what is needed to move them forward in a more equitable and sustainable way. Two initial historical chapters lay the framework for the regional discussions, while cross-cutting and integrated chapters analyze the state of natural and cultural heritage and current development theory provincially, offering guidance for the future.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".