Building Connected Communities: Halton, Burlington – 2016 Census Older Immigrants
Bibliographic record
Abstract
This data sheet provides a picture of the available relevant characteristics of the community at the time of the census (2016). We have included data both from those individuals over the age of 65 at the time of the census, as well as those who are in the age cohort just below (50 – 64), so those engaged in planning for future community needs can anticipate where growth or reduction in needs may be.\nThese numbers may be used to provide an overall picture of the municipality or region as a whole, and may be used to help guide municipality/region-wide decision making about where resources could be allocated in order to combat social isolation in the region. Used in concert with the interactive maps in this toolkit, planners can identify the geographic areas where these resources would see the greatest potential benefit.\nThis resource is included in the Social Isolation and Loneliness Toolkit, created by the Centre for Elder Research in Oakville ON, Canada. The Toolkit is part of a research project titled “Building Connected Communities: improving Community Supports to Reduce Loneliness and Social Isolation in Immigrants 65+”. The research focused on exploring strategies to effectively reach out to, and support, older immigrants who may be experiencing, or are at risk of experiencing, social isolation and/or loneliness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".