Rural population change in Nova Scotia, 1991–2001: bivariate and multivariate analysis of key drivers
Bibliographic record
Abstract
Depopulation is a major demographic and economic issue in Nova Scotia, as it is in many of Canada's hinterland areas. Indian Reserves excepted, two‐thirds of rural census subdivisions declined in population between 1991 and 2001, and this decline has serious economic and social consequences. By contrast, a small minority of seemingly ‘rural’ areas is experiencing excessive population growth through exurbanisation. This article combines map and graph analysis with simple regression and multivariate techniques to analyse the key drivers of recent population change. It is shown that such change is strongly and predictably related to unemployment rate, income and population density and moderately related to resource‐industry employment, proximity to a major urban centre and commuting. These six variables are interrelated, however, and their separate contributions are explored through principal component analysis and multiple regression. Two variables—resource‐industry employment and urban proximity—are identified as key root causes, indicative of separate factors. Findings are related to the Drudy–Gilg model of rural decline, and their policy implications are briefly discussed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".