Bibliographic record
Abstract
The chapTers in This book are the outcome of a high-profile collaborative research project: the Neighbourhood Change Research Partnership (NCRP), led by David Hulchanski at the University of Toronto.Scholars around the world have documented increased income polarization and ethno-cultural divides in large cities.These trends are known in the research literature as divided cities, dual cities, polarized cities, and the like.Though many of the trends are global, they play out at the local level.The NCRP seeks to understand these trends by examining inequality, diversity, and change at the neighbourhood level in Canada's metropolitan areas, where local research teams have carried out city-specific studies.This collection presents the project's findings from each city, bookended by chapters that place these findings in their broader context and draw out their implications for both scholarship and policy.The book is divided into three parts.The first part discusses the trends, theory, and methodological puzzles that motivated the research and guided its development.In the contextual introduction (Chapter 1), two of the team's intellectual leaders (Larry Bourne and David Hulchanski) identify the diversity of theories and concepts that guided our research on neighbourhood change and explain the kinds of research questions that the team has been exploring in our analyses of the transformations that occurred in Canadian cities between 1980 and 2015.Next, in Chapter 2, Richard Harris provides a brief history of neighbourhood and income inequality in Canada to lend background depth to the contemporary studies that appear later in the book.In Chapter 3, Ivan Townshend and Bob Murdie describe the team's efforts to develop descriptive
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.469 | 0.274 |
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".