Bibliographic record
Abstract
Urban geographers, sociologists, and planners have studied the dynamic spaces surrounding large cities for a long time (Dickinson 1947).In the attempt to come to grips with this complex spatial phenomenon in different parts of the world, they have evolved a whole set of new concepts such as desakota, umland, periurban, urban field, edge city, zwischenstadt, among others, which for the layperson remain largely opaque.In a millennial conference with a distinctly economic flavour, Allen J. Scott (2002) proposed the concept of city region as a useful trope for capturing the multi-dimensional aspect of the urban transition, which is the prospect that by the end of the twenty-first century the world as a whole will be spanned by a hierarchical system of urban centres. 1 One of the more prominent of these centres is Vancouver, British Columbia's pre-eminent city region, also known as the Lower Mainland.This region, once a medium-sized central city surrounded by a swarm of twenty-one smaller suburbs, is currently undergoing a period of rapid change, as suburbs are gradually growing together, shedding their profile as primarily dormitories while beginning to acquire distinctly urban characteristics in terms of population density, jobs, transport, communications, and other amenities.Future trend projections by Metro Vancouver, the regional agency, suggest that by mid-century the city region of Vancouver will have grown to become a metropolis exceeding 4 million, a gateway to Asia, and second only to Toronto as Canada's largest urban constellation.My attempt in this prologue is to sketch out this proposition and to look at
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".