Trends in Indigenous fertility in Canada, 2001–2021
Bibliographic record
Abstract
BACKGROUNDIndigenous peoples in Canada are among the youngest and fastest-growing populations in the country and have had higher fertility rates than non-Indigenous populations. OBJECTIVEThis paper examines how Indigenous fertility in Canada changed over two decades .It also examines how Indigenous fertility varies across different Indigenous populations and how the gap between Indigenous and non-Indigenous fertility has changed. METHODSThe paper uses the own-children method to reconstruct the total fertility rate (TFR) of Indigenous populations in Canada.Data are from confidential long-form Canadian census micro-files from 2000, 2006, 2016, and 2021 and from the National Household Survey of 2011. RESULTSFirst, we find that Indigenous fertility was close to replacement level in 2001, 2006, and 2011 and that it declined below replacement fertility in 2016 to 1.82 and then to 1.54 in 2021.Second, we disaggregate Indigenous fertility and find that the Inuit have the highest TFR among all Indigenous populations.Status Indians had above-replacement fertility in 2001, 2006, and 2011 but as of 2021 have had below-replacement fertility.In contrast, non-status Indians and Métis had below-replacement fertility between 2001 and 2021.Third, although Indigenous peoples have had much higher fertility than non-Indigenous groups in Canada, the gap has narrowed. CONCLUSIONSIndigenous fertility has declined to below-replacement levels, moving toward convergence with non-Indigenous populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".