China rebalancing continues, but debt risks rise
Bibliographic record
Abstract
Subject The macroeconomic outlook for China. Significance GDP grew by 6.7% year-on-year in the first quarter of 2016 to almost 15.9 trillion renminbi (2.4 trillion dollars), the National Bureau of Statistics (NBS) reported on April 15. Over-reliance on investment and policy stimuli are carrying the economy, with only a slow structural rebalancing towards demand needed for long-term sustainable growth. Mounting debt is of immediate concern for the corporate and financial sectors, and for the outlook this year, the first in the 13th Five-Year Plan period. Impacts Markets will be especially sensitive to data releases and policy signals, making for more volatility. The possibility of a US rate hike complicates China's efforts to hold its interest rates down while controlling outflows. Conversion of debt into bonds and some relaxation of social security contribution requirements should take some pressure off localities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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; both teacher heads agree on what is shown here.
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".