CAN IMMIGRATION COMPENSATE FOR BELOW-REPLACEMENT FERTILITY?: THE CONSEQUENCES OF THE UNBALANCED SETTLEMENT OF IMMIGRANTS IN CANADIAN CITIES, 2001-2051.
Bibliographic record
Abstract
This is a study of the long-term consequences of the combination of sustained below-replacement fertility rates and the unbalanced distribution of immigrantswithinCanada. Fiftyyearpopulationprojectionsarecalculatedfor the 26 largest cities in Canada, assuming a continuation of current fertility, mortality and internal migration rates, under four scenarios: three of them vary immigration levels (high, medium and low) with a continuation of current settlement patterns, and a fourth scenario assumes a balanced distribution in which each city receives its proportional share of immigrants. The study finds that natural decrease will occur in the short term, and that migration will play a critical role in the future of Canadian cities. Without in-migration (either internal or international), cities will decline rapidly. Under current immigration levels, only 10 of the 26 cities are projected to grow through to 2051, and 12 will be smaller in 2051 than in 2001, to as low as less than half their current population. On the other hand, some cities with high levels of in-migration are projected to more than double in population size. There will be a growing division between cities thataregrowingandthosethataredeclining. Growingcitieswillhaveyounger age structures and more diverse populations than those in decline. Increasing immigration levels without a shift in the distribution of immigrants does little to help cities with little attraction for immigrants, but does increase the rate of growth for those that are already rapidly growing. Future age structures for each of the cities are also examined and it is found that there will be more people in the highest age groups in all cities, even those that are declining. The numbers of young people, though, will decline in many cities. Challenges associated with changing demographic conditions are discussed, including those that relate to the global ∞nte×t in which some regions of the world are projected to continue to grow rapidly while others are projected to decline.\nThis study challenges the implicit assumption in demographic transition theory that there is a natural balance between births and deaths in any\npopulation, and suggests that demographic transition theory be revised to include\na fourth phase, in which fertility rates remain below replacement levels resulting in natural decrease.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".