1 The Demographic Context of Rural Canada: The Size of the Indigenous and Visible Minority Populations
Bibliographic record
Abstract
THIS VOLUME is understanding how to build inclusive communities in rural Canada, where people of all racial, cultural, and religious backgrounds feel at home among neighbours.This is an important goal because rural Canada is more racially diverse than many people realize.Building inclusive communities in rural Canada requires, first and foremost, an understanding of who lives in these communities and who is settling there.My aim in this chapter is to paint a picture of the racial composition of rural peoples and communities across Canada, focusing on the size and growth of Indigenous and visible minority populations.As I will show, rural diversity looks much different than urban diversity in Canada.Rural communities also look very different from each other, particularly when comparing communities across regions and provinces.Those working in areas of diversity and inclusion in rural communities need to take the specific demography into account when developing their approaches.As the ol' saying goes, "When you have seen one rural community, you have seen one rural community." 4The Demographic Context of Rural Canada WHAT DOES IT MEAN TO BE "RURAL"?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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".