Bibliographic record
Abstract
This book brings new perspectives on the multicultural, ethnically diverse, economically dynamic, and still relatively segregated urban space of early twenty-first-century Canada.It was no small task to put together!Not just because it involved a large number of researchers and volumes of data but also because it aims to understand how and why local neighbourhoods change in the context of a fast-moving, globalized world.A hundred years ago, a small cadre of sociologists at the University of Chicago (the Chicago School) took on a similar challenge, seeking to give neighbourhood work "a scientific basis" (Park, Burgess, and Mackenzie 1925).In their framework, neighbourhoods were natural sites for investigating the modern city.Driven by industrial development and the growing material wealth it generated, the titans of industry were building up cities with the help of a seemingly endless influx of migrants from overseas and the rural hinterlands.The order and chaos observed were great fodder for scholars in the burgeoning field of urban sociology, and specifically human ecology, which sought to explain the spatial patterns of cities as an extension of human nature.Their conclusion: it was natural to segregate by race, ethnicity, and class.Moreover, they suggested, as urban growth continued, the state of equilibrium required sustained differentiated space to sort and separate where people lived.While many urban scholars trace contemporary thinking about neighbourhood change back to the Chicago School, the dominant narrative has shifted toward explanations that emphasize political economy and the social production Foreword
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.620 | 0.552 |
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".