Demographics, Employment, Income, and Networks: Differential Characteristics of Rural Populations
Bibliographic record
Abstract
This paper reviews the key demographic, employment, income, and social capital features of rural Canada. Rural populations have different characteristics that are typically a direct result of "rurality"--i.e., long distances and low population density. Jobs that require a high-density population (such as a professional hockey player) are not available to individuals who live at a distance from a metro center. Rural Canada may have an agricultural landscape (or a forestry or mining landscape) but the vast majority of rural workers do not work in primary sectors. Manufacturing employment is larger. Rural Canada is competitive in manufacturing--rural areas are gaining a larger share of Canada's manufacturing workforce. Rural incomes are lower, on average. But lower living costs mean that the rural incidence of low incomes is similar to urban. In rural communities, the existence of social networks does not always imply that these networks are used. Networks are complementary-one network does not always substitute for another. However, local strength in one network can be used to build capacity in another network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".