The Influence of Educational Opportunity on Population Maintenance or Growth in Rural Alberta
Bibliographic record
Abstract
In 2021, the Government of Alberta announced the Building Skills for Jobs: Alberta 2030 initiative which directs post-secondary institutions to prepare students for jobs in the local economy. The Economic Development in Rural Alberta Plan was a supporting initiative announced in 2022, which promotes more awareness of careers in rural communities and promotes experiential learning to obtain skills needed for work. Both initiatives identified education as one of the provincial priorities to attract and retain newcomers and increase the economic activity of rural areas. By examining correlations between regional education and other economic indicators, I show that there is a strong relationship between many of these factors, with a reasonable causal interpretation. This suggests that improving educational opportunities, in addition to promoting economic growth and activity, can lead to population maintenance and growth in rural regions in Alberta. Newcomers are attracted to economic opportunities, access to services (which include education) and infrastructure. If governments intend to attract and retain populations to rural areas, they would be well served to invest in infrastructure and facilities that improve rural quality of life, including improvements in education services. Such investments (particularly at the post-secondary level) would be bolstered by aligning the curriculum of post-secondary institutions with the local economic needs of the area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".