Visualising the distribution of individuals of advanced age in Canada: linking census data to maps
Bibliographic record
Abstract
OBJECTIVE: to link publically available aggregate census data to maps to visually convey information about the geographic distribution of those of advanced age in Canada. METHOD: we obtained aggregate statistics derived from the most recent 2011 Canadian census data. We calculated the Percentage of People 90 Years of Age or Older and the Longevity Index in each of the 292 census divisions. The data and the Canadian census division map were merged to create thematic maps using Google Fusion Tables. RESULTS: overall, there were 217,930 women and men who were 90 years of age or older in Canada in 2011. The regions with the highest proportion of elderly residents are in the south, rather than in the north of Canada. Southern Saskatchewan and southern Manitoba emerged as high longevity areas based on both indices. CONCLUSIONS: publically available data and free online tools can be used to create maps that visually display the geographic distribution of the oldest population across Canada. This approach provides an efficient way to observe patterns, identify adjacencies and perceive information that may not have been anticipated. This approach can be replicated in other jurisdictions using publically available data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".