Bibliographic record
Abstract
In its October 2016 World Economic Outlook, the International Monetary Fund adjusted its growth estimates for 2016 and 2017 downwards. Slower growth in the developed economies is the main reason for the sluggish performance of the global economy. The expected improvement in the US economy in the second quarter did not materialise. Despite favourable weather, growth in the euro zone decreased during the first half of 2016. In the UK, faster growth in the first quarter was followed by slower growth in the second quarter. The slower growth in developed economies did not affect the emerging market and developing countries significantly. As a group, their economies picked up in the first quarter of 2016. China's economy grew by 6,5%, while India continued its robust recovery. The developed world was hard hit by the 2008 financial crisis. Although much was done to repair the damage, progress remains uneven. In the euro zone, GDP growth remains below pre-crisis levels.Weak global demand is still a problem and unemployment has decreased, but it is still above the pre-2008 level. Population growth in developed countries has slowed and will decline further in coming years. Population aging will put more pressure on pension and healthcare systems, resulting in increased debt problems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.042 |
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".