The Research of the Birth Rate in Canada from 1951 to 2022
Bibliographic record
Abstract
Although previous studies have shown that Canada's birth rate is in constant decline, many factors are worth investigating. In this study, the ARIMA time series model approach was used to process data from the United Nations World Population Prospects, a system that collects Canadian birth rates from 1995 to 2022. Finally, when the residual model has a sequence relationship, the ARCH/GARCH model is considered for further study, and the conclusion is that the birth rate is still declining due to many factors such as the epidemic situation and living habits, and the birth rate growth is still negative in the next 20 years. However, with more data, Canada's population is still the fastest-growing country among many developed countries. According to the literature analysis, the factors contributing to the low birth rate include the COVID-19 pandemic, women's thoughtfulness and social status, and the improvement of quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".