Bibliographic record
Abstract
Urbanists share and reproduce three stereotypes about North American suburbs. First, many invoke a clichéd ideal: the desire to enjoy quiet privacy in a low-density residential environment near the urban fringe. Second, they assume that most suburbs have actually conformed to this ideal. Third, academics and planners alike agree on a stereotypical judgment: suburbs are to be deplored. This synthetic essay argues that residential patterns in postwar Toronto never conformed to these stereotypes: especially since the 1970s it has harboured a competing, more urbane popular ideal; its suburbs have been socially and physically diverse; and, recognizing diversity, local urbanists have made varied judgments. Suburban diversity has become systematized since the 1970s, so that a new local stereotype has emerged: that of the declining inner suburb. Toronto's experience exemplifies that of one of the two main types of North American metro. It challenges stereotypes, while those stereotypes illuminate its particular character. Most generally, while polycentricity and dispersion have shaped its economic geography, the language of zones is still meaningful in interpreting its residential patterns. There may be a larger lesson there.
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 | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".