Measuring Neighborhood Social Change in Saskatoon, Canada: A Geographic Analysis
Bibliographic record
Abstract
The majority of research on neighborhood change in Canada has followed a cross-sectional approach and has relied on census tracts as the basic unit of geography. Due to concerns over methodology and data comparability, very few studies have attempted a direct analysis of change. In response, this article presents a protocol for measuring neighborhood social change applied to Saskatoon, Canada and employs census data for neighborhoods that have been officially designated by the city's Planning Department. Our study found that about half of Saskatoon's 58 neighborhoods experienced stability between 1991 and 2001. However, decline was just as likely to occur in middle- and high-socioeconomic status (SES) neighborhoods as in low-SES neighborhoods while improvement was more likely to occur in the low-SES group. A pronounced division was visible among low-SES neighborhoods, particularly in the city's core. The analysis also found that income, gender, and housing had a strong impact on neighborhood social change and inequality. Interpretation of the findings revealed that a number of factors ranging from local conditions to wider economic and policy shifts had an influence on changing conditions in Saskatoon's neighborhoods.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".