London CANADA N6A 5C2Patterns of Urban Residential Settlement Among Canada’s First Nations Peoples
Bibliographic record
Abstract
There is a long tradition of research into residential settlement patterns in North American sociology. Much of the impetus for this research comes from the Chicago School that focussed on the ecological patterns of urban settlement of immigrants in America’s large cities in the early Twentieth Century. That research broadened, especially after World War II, when the United States experienced high rates of internal migration. Sociologists conducted similar research in Canada but to a lesser extent. In both the United States and Canada, however, almost none of that research has focussed on the indigenous population. The settlement dynamic of First Nations peoples in urban areas is of particular interest in the Canadian context as we have seen both a revitalization of reserves (First Nations communities) and an apparent increase in movement to urban centres. Recent research suggests that, while the First Nations populations on reserves have been growing at rates faster than the general Canadian population, the number of Canadians declaring themselves as Aboriginal has been increasing in the urban areas at even faster rates. In 1951 only 7% of the Aboriginal population lived in an urban area (more than 1,000 persons) while the 1991 census shows that 42 % of those defined as single origin North American Indians are in such communities (Statistics Canada, 1991 Census, Cat.93-340, Table 1; Drost et al., 1995:13). Despite this
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".