Migration Between Indian Reserves and Off-Reserve Areas: an Exploratory Analysis Using Census Data Linkage
Bibliographic record
Abstract
Abstract New data linkages between censuses show that migration flows between Indian reserves and off-reserve areas from 2006 to 2011 and from 2011 to 2016 resulted in negative net migration for Indian reserves, meaning that—overall—more people left Indian reserves than entered them. These results differ from the portrait shown by the retrospective information from the 2011 and 2016 censuses, which indicates positive net migration for Indian reserves. A comparison of the information in the two sources revealed two types of inconsistencies that contributed to the observed differences: (1) inconsistencies in migrant status, and (2) inconsistencies in the origin location of migrants, i.e., the retrospective information about a migrant’s place of residence 5 years earlier does not match the place where the migrant was enumerated in the previous census. Results from this paper suggest that there are limitations to using retrospective information on the place of residence 5 years prior to a census to derive estimates of internal migration flows for small geographic areas, such as Indian reserves. New data linkages are a source of information that can be used to validate and improve these estimates, as well as to derive alternative estimates. However, data linkages also have limitations and require careful preparation before use, particularly when it comes to calculating weights to accurately account for unlinked records.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".