Regional Income and Consumer Expenditure Specifics of the Canadian Population
Bibliographic record
Abstract
In the confrontation between the global West and the global South, Russia found itself directly on the fault line. The confrontation leads to changes in the world economy, which, of course, have an impact on the standard of living of the population. It becomes relevant to study this influence, both in Russia and in countries that impose economic sanctions against it. This study provides a brief analysis of household income and consumption expenditure in Canada for 20002022 across the provinces of the country. Based on the data obtained, a high degree of social support for Canadian citizens from the state was revealed, especially in areas densely populated by indigenous peoples. The conditional division of the country according to the standard of living of the population into four geographical sectors is determined: northern, western, central and eastern. It has been established that throughout the entire period under review, the indicators of the standard of living of citizens in the northern provinces are the highest, and in the eastern provinces the lowest in Canada. At the same time, about two-thirds of the total population of Canada live in the eastern provinces, and the Northern provinces are very sparsely populated (less than 1% of the total population). It has been recorded that relatively high incomes and savings of the population are not a determining factor in the country for choosing a place of residence. Much depends on other indicators of the quality of life of people, as well as on the local mentality, traditions and culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".