World Overview
Bibliographic record
Abstract
GDP growth in the OECD group of economies moderated in the first quarter of 2011, reflecting a contraction in output in Japan related to the earthquake in March 2011 and a slowdown in the US economy. This was partly offset by an acceleration of growth in the Euro Area, to some extent attributable to a weather related rebound in Northern Europe, but also a strong rise in business investment in Germany and France. Moderate growth at the OECD level persisted into the second quarter. Supply-chain disruptions continued to affect Japan; the high oil price eroded real wages, exacerbating the effect of high unemployment on consumption in the US; the deepening sovereign debt crisis in Europe raised uncertainty, leading to a rise in precautionary savings even in countries not restrained by severe fiscal austerity programmes. Outside the OECD, China and India continue to drive world growth, although rising inflation points to more moderate prospects in the second half of the year. We forecast global GDP growth of about 4½ per cent per annum in both 2011 and 2012, compared to 5 per cent growth recorded in 2010. The key assumptions underlying this forecast are discussed in Appendix A, with our forecasts for key macro variables in 40 major economies detailed in Appendix B.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.398 | 0.252 |
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 source (direct Gemma or distilled Codex), 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".