Bibliographic record
Abstract
Cities, increasingly, are the principal arenas in which global, national and local forces inter-sect. Canadian cities are no exception. Those cities are currently undergoing a series of profound and irreversible transitions as a result of external forces originating from different sources and operating at different spatial scales. Specifically, this paper argues that Cana-dian cities are being transformed in a markedly uneven fashion through the intersection of changes in national and regional economies, the continued demographic transition, and shifts in government policy on the one hand, and through increased levels and new sources of immigration, and the globalization of capital and trade flows, on the other hand. These shifts, in turn, are producing new patterns of external dependence, a more fragmented urban system, and continued metropolitan concentration. They are also leading to increased socio-cultural differences, with intense cultural diversity in some cities juxtaposed with homoge-neity in other cities, and to new sets of urban winners and losers. In effect, these transitions are creating new sources of difference - new divides - among and within the country=s urban centres, augmenting or replacing the traditional divides based on city-size, location in the heartland or periphery, and local economic base.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".