Spatial Data Analysis: Examining the Quality of Life for Ethiopians and Nigerians in the City of Toronto and Edmonton
Bibliographic record
Abstract
Canadian cities have experienced an increase of immigrants, more notably, from Nigeria and Ethiopia. Both groups have settled in Canadian cities to improve their overall Quality of Life (QOL). Areas such as education, employment, safety, and housing conditions were top priorities in choosing a new location to live. Studies, however, show a large proportion of immigrants from Nigeria and Ethiopia have settled in regions that contradict their desires and actually obtain a ‘high’ standard of quality of life. The research findings show both groups have settled in areas with high unemployment rates; highly educated populates with lower-paying jobs; and lower incomes in comparison the host city. The spatial distribution of recent immigrants is paramount to understanding how well those groups will assimilate. Local governments can use this data to create spatial distributive policies that are hospitable or antagonistic to specific groups. Many studies focus on investigating social integration at the provincial level; however, the experience of integration is intrinsically a local one. Thus, local governments have the opportunity to increase the overall quality of life for immigrants by using the structural, spatial divisions used in this study. The purpose of this study is to examine the QOL of Nigerians and Ethiopians in two Canadian cities: Edmonton and Toronto. This study has two separate, but related empirical components: (1) the first examines the spatial distribution of both groups, and the (2) second examines the overall quality of life in the primary settlement areas.
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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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".