Migration into and out of Indian Reserves Between 2011 and 2016: a Study Using Census Data Linkage
Bibliographic record
Abstract
Abstract The impact of migration on the sizes, composition, and well-being of First Nations communities and the motivations that triggered such migrations have long been a topic of interest among researchers. Exploiting a new data source, linkages of consecutive censuses, this study aims to portray migration into and out of Indian reserves, with a focus on the Indigenous population. Between 2011 and 2016, migrations into and out of reserves resulted in net losses for reserves. These migratory losses, however, did not prevent the population on reserve to continue growing. From a socioeconomic point of view, migrations had a net positive impact on reserves by contributing to increase the proportions of individuals who are employed, with relatively high incomes or relatively high education. Looking at the determinants of migration, and taking advantage of a multilevel framework, it is found that migration into and out of reserves is multidimensional, being influenced by factors at both individual and community levels. Out-migration seems to be governed mainly by the propensity of individuals at certain stages of life to leave the reserve, permanently or not. In contrast, in-migration appears more influenced by reserves’ characteristics, and its prevalence varies greatly from one reserve to another.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".