ISSUES IN ESTIMATING THE POPULATION OF CANADIAN MUNICIPALITIES
Bibliographic record
Abstract
Interest in current population data at the municipality level has increased significantly in recent years. The demand comes from a variety of sources, including individual municipalities, regional governments, and provincial as well as federal authorities. While the Canadian census has served in the past as the primary source of demographic data, users are now interested in obtaining estimates and projections based on the most current figures available. This shift in focus is attributable to two main factors. First, in the current context of fiscal restraint, the planning of municipal programs and services, including their funding by the various levels of government, has increased the need for more up-to-date demographic data. The second factor is related to the adjustment for net census undercoverage (persons missed in the census or counted more than once) that is now built into Statistics Canada’s population estimation program. Though the program does not extend to municipal level estimates, users require that population data for these areas be consistent with the adjusted population figures available for provinces and territories, census divisions and census metropolitan areas. This paper reviews the potential data sources and methods for estimating municipal populations in Canada. Issues discussed include the limitations and possible improvements of the data currently available, as well as the strengths and weaknesses of the assumptions underlying the models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".