China political stability will endure growth slowdown
Bibliographic record
Abstract
Significance Headline growth was 6.7% for the third successive quarter -- neatly within the target range of 6.5-7.0%. However, the rate has not risen in any quarter since the last peak of 7.2% in the final quarter of 2014. Well into the 2010s, it was still widely claimed that growth of at least 8.0% was necessary to prevent widespread unemployment that could lead to serious social and political unrest. Growth has fallen short of this since late 2012, but the anticipated crisis has not arrived. Yet such concerns persist, albeit with a lower 'danger line'. Impacts Repression and anti-corruption operations risk sowing seeds of instability that would not otherwise be there. Political risks arising from the economy will increase in the near future as state-sector and military layoffs get properly underway. There is no sign that the state's repressive capabilities are set to weaken.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.001 |
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".