Bibliographic record
Abstract
Development of the Canadian Accessibility and Remoteness Index Plus (CARI+). Rurality and remoteness are policy-relevant concepts for planning and evaluating equitable access to services across Canada, yet existing measures rely on categorical, urban-centric and coarse spatial definitions that obscure variation in accessibility across the rural–urban continuum. The Canadian Accessibility and Remoteness Index Plus (CARI+) is a small-area accessibility index at the dissemination-area (DA) level based on road-network travel to stratified service centres, including population centres and 24-hour emergency departments stratified by settlement-size. An overall rurality score is produced, then normalized to an index between 0 and 1 and classified via multiple methods. The CARI+ produces an even population distribution across categories of rurality, revealing socio-demographic gradients in material deprivation and Indigenous identity that increase with remoteness, while educational attainment and immigrant representation decline. In very remote areas, CARI+ identifies 25% Indigenous residents and 27% of the population with less than high school education, versus 78% and over 50% respectively under the other rural measures. CARI+ provides a transparent, service-specific, high-resolution measure of accessibility that addresses key limitations of existing Canadian indices and supports nuanced analysis for health and social service planning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".