Bibliographic record
Abstract
The birth rate in China has declined significantly since the last century, especially in recent years. Low fertility intentions and deferred childbirth were the main contributors to the drop in fertility. Nowadays, the aging problem has become more and more serious in our country. Therefore, raising the birth rate has become the top priority for the government and society. Our research modelled and predicted the fertility rate in China using R. We used the dataset from Health Nutrition and Population Statistics on Databank and selected the historical fertility rate data in China from 1964 to 2021. Firstly, we transformed these data to make the birth rate time series stationary. Then, we applied the ARIMA and the ETS models, respectively and chose the better model to forecast the short-term birth rate in China in the next five years. We found that the ARIMA model was more appropriate for predicting the short-term birth rate in China. As a result, we utilized the ARIMA(0,0,1) model for prediction. We calculated that the growth rate of the birth rate in China from 2022 to 2026 was - 0.117, which demonstrated that the fertility rate in China would decrease constantly at a rate of 0.117 each year over the ensuing five years. If the government of China still did not take any measures to improve the fertility problems, the fertility rate would further decline. Consequently, the fertility rate in China is facing an unprecedented crisis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".