The urban and regional dimensions of economic inequality in Canada, 1996 - 2006
Bibliographic record
Abstract
There is a general consensus that economic inequality increased in Canada between 1996 and 2006. However, few studies examine the multi-dimensional causes of this trend at the sub-national scale. Chakravorty (1996, 2006) and others (Morrill, 2000; Martin, 2001, Drennan, 2005) argue that an understanding of what influences national-scale inequality requires in-depth consideration of urban and regional socioeconomic processes. Using microdata drawn from the most recent 20-percent samples of the Canadian Census of Population (1996, 2001, and 2006), this thesis examines the spatial and socioeconomic dimensions of earnings inequality among individuals in Canada's labour force. The thesis makes two main contributions to the literature. First, it provides a detailed analysis of the key socioeconomic determinants of earnings inequality across Canadian urban areas using regression analysis. The findings provide new evidence that substantial changes have occurred in the contribution of specific factors to inequality since 1996. Second, spatial data analysis points to changes in the geographic distribution of earnings inequality. Between 1996 and 2006, high levels of inequality across Canada's census divisions have increasingly clustered in Alberta and Newfoundland, and results from spatial regression models shed further light on changes in the nature and structure of earnings inequality.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".