1 The Demographic Context of Rural Canada: The Size of the Indigenous and Visible Minority Populations
Bibliographic record
Abstract
Rurality is commonly described in terms of two geospatial dimensions: density (or population size) and distance-to-density (or distance to an urban centre) (World Bank 2009;Bollman and Reimer 2019).These two dimensions of rurality are depicted as continuums in Figure 1.1, with density along the horizontal axis and distance-to-density along the vertical axis.Communities in the upper-right corner of Figure 1.1 are smaller (i.e., more rural in the density dimension) but located relatively close to a larger centre (i.e., are less rural on the distance-to-density dimension).Communities in the lower-left corner are larger (i.e., less rural in the density dimension) but are located further from a larger centre (i.e., more rural in the distance-todensity dimension).The closer you get in either or both dimensions to the lower-right corner, the higher the overall rurality of the community.The degree of rurality along these two dimensions determines many features of the lived experience of rural residents.For instance, smaller communities may have few available and/or quality services; however, if a smaller community is close to a larger centre, then commuting to the larger centre would enable access to more and better-quality services.Similarly, businesses in smaller communities are limited in the range of goods and services they can sell locally.However, if the community is close to a larger centre, then it is easier to sell into the nearby larger figure 1.1 The two dimensions of the rurality of localities: density and distance-to-density Degree of rurality in the density dimension Degree of rurality in the Low rurality, High rurality, distance-to-density dimension high density low density Low rurality, short distance High rurality, long distance b o l l m a n 5 market and it is easier for individuals to access the types of jobs available in a larger centre.The dimensions of density and distance-to-density offer a straightforward, geospatial way of defining the rurality of the resident population.Working with such a definition separates the concept of the rural from the demographic characteristics-such as age structure, degree of poverty, or ethnic mix-of the individuals and organizations who live or operate in rural communities.The degree to which a community can be called rural depends only on where it falls along the density and distanceto-density axes, not on who lives there.That being said, a community's demographic composition may be correlated with these dimensions of rurality.The degree of diversity of communities might be expected to differ across the rurality dimensions of density and distance-to-density.For instance, the rurality of a community may impact its attractiveness to recent immigrants looking for a place to settle. THE SIZE OF CANADA'S RURAL POPULATIONThis chapter uses statistical thresholds to describe and define the rurality of a community.Almost every classification of rural populations in countries around the world will use a density (or population size) threshold and a threshold of distance-to-density or adjacency.Different thresholds will provide different perspectives on the size and location of the populations and communities that we classify as rural.Table 1.1 shows Canadian communities organized along the two dimensions of rurality.For the purposes of this table, a community is defined as a census subdivision (CSD)-the general term used by Statistics Canada (2016) for "incorporated towns or incorporated municipalities (as determined by provincial/territorial legislation) or for areas treated as municipal equivalents for statistical purposes (e.g., Indian reserves, Indian settlements and unorganized territories)."The Group A and Group B labels in Table 1.1 illustrate alternative thresholds for population size anddistance-to-density. Group A represents communities that I will call "small and remote"; Group B, communities that I will call "smaller and more remote."Using these two (somewhat arbitrary) thresholds and adding up the CSDs in each group, we see that there are 1877 small and remote communities in Canada (36% of all communities).Of these, 527 are smaller and more remote (10% of all communities). CSD population size (density dimension)500,000 100,000 50,000 25,000 10,000 5,000 2,500 1,000 750 500 100 Less All CSDs and over to to to to to to to to to to than 499,999 99,999 49,999 24,999 9,999 5,000 2,499 999 749 499 100 CSD remoteness index Number of CSDs (distance-to-density dimension) Less than 0.09 6 19 15 24 49 30 19 13 3 3 3 2 0.1 to 0.19
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.037 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".