Bibliographic record
Abstract
• The positive indications for growth in the advanced economies that we noted a month ago have continued to appear during recent weeks. • In the US, the manufacturing and non-manufacturing ISM surveys improved sharply in July. Revisions to past data mean our 2013 forecast has been edged down this month to 1.6%, but our 2014 forecast has been raised to 3.1%. • Eurozone indicators meanwhile point to the region finally moving out of recession in Q2, probably growing by 0.1% on the quarter after six consecutive falls. Especially promising were the strong manufacturing orders data for June in Germany which may point to a revival of investment activity in the region. • Incoming data for Japan and the UK have also been upbeat, leading to further upward revisions to our growth forecasts for both countries this month. • The upturn in the advanced economies has occurred despite a weaker picture in the emerging economies – Chinese data remain lacklustre and this month sees further forecast downgrades in Brazil. Rather than world trade growth leading the upturn, improving domestic demand in the advanced economies – including investment – has been the key factor. • This in large part reflects the impact of fiscal and monetary stimulus. But while recent data are promising, it is far from clear that the advanced economies have reached ‘escape velocity’ and that stimulus can therefore be pared back. • In our view, it makes sense to err on the side of caution and maintain stimulus until recovery is clearly established. This implies caution on ‘tapering’ asset purchases in the US and some countries (e.g. Japan and some Eurozone states) perhaps reconsidering planned fiscal tightening. Recent ‘forward guidance’ by central banks in the UK and Eurozone, aimed at keeping a loose monetary stance, also makes sense.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.100 |
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".