China: Rebalancing Growth with Reform and Moving Up to the Next Level
Bibliographic record
Abstract
This chapter asserts how the developments in China have a direct consequence on Southeast Asia, Malaysia, in particular. Of late, China's growth has slackened, having been subdued by normal standards. Its growth in fixed investment, housing, retail sales, and factory output has hit multiyear lows in the first quarter of 2014. This has rattled global investors, worried that this will soon become a drag on activity worldwide. The time has come to change its development mode so that a fall in gross domestic product (GDP) growth becomes unavoidable to improve the quality and efficiency of development. Already, analysts have trimmed forecasts: 7 percent in the first quarter of 2014 as against 7.7 percent in the fourth quarter of 2013. For the full year, 7.2 percent appears to be the consensus given reform remains top priority. This seems to be Beijing's bottom line to keep employment from faltering. If the pace of growth does not pick up, backing for deep economic reforms will wane.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".