Examining Demographic Characteristics and Settlement Patterns of Ethnoculturally-Diverse Specific Long-Standing and Recent Older Immigrants in the Toronto CMA, 2016
Bibliographic record
Abstract
Understanding population characteristics and residential patterns of recent and long-standing older immigrants is important to ensure that settlement services are adequately supporting a diverse and vulnerable population. This research paper represents a pilot study to fill in the gap found in the already limited scholarship on the characterization, spatial distribution and in-group differences of older immigrants in the Toronto CMA. Firstly, it explores the nuanced differences in population composition of four ethnocultural-specific subgroups representing long-standing (Italian and Portuguese) and recent immigrants (Chinese and South Asian) and secondly, it identifies clusters of recent immigrants that are settling outside of the long-standing ethnocultural enclaves. Despite having higher rates of education than their long-standing counterparts, Chinese and South Asian are characterized by low income prevalence and lack of knowledge of an official language. Hence, determining the multilingual composition of the South Asian and Chinese subgroups can facilitate language-specific settlement services within recent older South Asian and Chinese immigrant clusters. Key words: older adults, immigration studies, recent immigrants, settlement challenges, low income, hot spot analysis, Toronto Census Metropolitan Area
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".