Regional differentiation of the tax burden on personal income in Canada
Bibliographic record
Abstract
The income of citizens reflects the degree of economic development of the country, and in the regional aspect – the degree of economic development of each region. Canada is very similar to Russia in its geographical location, nature and climate, so the standard of living of the population in this country is of interest. One of its indicators is the income of citizens. The state’s approach to personal income taxation reflects the general socio-economic policy in the country. The study analysed the tax burden on personal income in the provinces of Canada in 2000–2022, determined the dynamics of this indicator for the period under review, and identified the existence of correlation between the amount of income and the tax burden on it in the regional aspect. The results obtained found that in Canada, there is a conditional division into eastern provinces with a traditionally high tax burden, western provinces with a national average tax burden, and northern territories with a relatively low tax burden. A certain inverse relationship between the amount of citizens’ income and the tax burden on it has been established. In regions with high incomes, the tax burden is usually lower, and in regions with low incomes, it is generally higher. However, there are regions that are exceptions, which shows the relative financial independence of regional authorities in Canada. The dynamics of the tax burden on personal income in the analysed period reflected the impact of the crises of 2008 and 2020 when it decreased. This fact demonstrates the sensitive response of regional governments to the changing economic situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".