Bibliographic record
Abstract
(not to be quoted without permission of the authors) This paper revisits the issue of Europe’s growth slowdown, taking into account the latest experiences from the recession and the debt crisis since 2008. From a supply side perspective, using a growth accounting approach, there are virtually no signs of even the beginnings of a reversal in the slowing growth trend, which is primarily driven by a weak productivity performance in most European countries. Recently, low productivity growth has broadened from the services sector to the goods producing sector for most European economies as well. However, the manufacturing sectors have begun to recover from the recession, and the most troubled economies even show signs of improved cost competitiveness. But the manufacturing sector on itself is too small to force an economy-wide reversal in productivity. From a demand perspective, using a global value chain-type analysis, it turns out that activities contributing directly or indirectly to production for the global market, account for roughly a quarter of jobs as well as a quarter of labor productivity growth in Europe. Manufacturing accounts for a significant share of this, but in many European countries market services have increased their contribution to global value chains, both in terms of job creation as well as productivity. Projecting growth out to 2025, using growth accounting projections, productivity remains the critical
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.646 | 0.549 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".