Local Scale Population Projection Methods: Shrinking and Aging Communities
Bibliographic record
Abstract
Poster Presentation The emergence of a globalized economy has given rise to ‘global cities’ where knowledge, resource and human capital conglomerate – often at the cost of outmigration of resources in smaller cities. In the Canadian context, the growth of a few major centers is contrasted with many smaller and peripheral cities that may be coping with shrinking populations and economic decline. These effects are increasingly compounded by a second demographic transition, which is characterized by falling birth rates and an aging population. Continued loss of population, changing demographic structure, and economic decline can lead to a myriad of challenges, including underused infrastructure, high vacancy rates, and socio-economic inequality. As Statistics Canada’s population projections are limited to the provincial, territorial and national level, individual municipalities are left to calculate their own projections, which could be hindered by a lack of resources, the complexity of calculating local-scale migration rates, or simply may not be done. This paper reviews the methodological differences reflected in the approaches taken by various levels of government and concludes that more complex, time consuming and expensive models are used at higher levels of governance and in larger cities and are more likely to provide more accurate and precise results. Smaller and peripheral cities tend to use simpler, less time- and resource-intensive methods. An assessment framework of nine criteria concluded that the share capture method is the best methodological alternative for local scale population projection. The share capture model is applied to every municipality (with population above 10,000) in Ontario and projected dependency ratios are calculated to ascertain the future distribution of aging communities in Ontario.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".