Forecasting urban unemployment rate in China using ARIMA model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The urban unemployment rate is a significant economic indicator that has long drawn researchers’ interest. Monitoring and predicting changes in the unemployment rate can help in understanding economic trends and implementing appropriate measures. This article aims to forecast urban unemployment rates in China. By collecting previous surveyed urban unemployment rates in China, this article will generate and compare various ARIMA models in order to identify the one with the best forecasting accuracy. The forecast results of the selected model state that the unemployment rate will remain almost unchanged, around 5%, in the second half of 2024 and throughout 2025. Fluctuations are expected to be between 0.01% and 0.03%. The number is much lower than the peak during the pandemic, but it is still above the historical average. This article argues that China’s economy is gradually stabilizing, and the post-pandemic measures have been effective but are still insufficient. The government still needs to implement additional actions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it