Jean-Claude Trichet: Global economic governance and euro area economic governance Speech by Mr Jean-Claude Trichet, President of the European Central Bank, at the World
Bibliographic record
Abstract
It is a real pleasure to be here in Marrakech – a city which, in its history, is a demonstration of the link between economic success and political events and choices. Morocco is also an important partner for the EU in the context of the Union for the Mediterranean and the Barcelona process. Let me also mention that the ECB and the Bank Al Maghrib have wellestablished bilateral relations, as well as close contacts in a multilateral framework. For example, the ECB and the other Eurosystem central banks regularly meet with Bank Al Maghrib and the other central banks of the Mediterranean region in the framework of highlevel seminars, in which they discuss economic and financial issues of common interest. Let me start by saying a few words about the current economic outlook in the euro area. Real GDP in the euro area grew by 1%, quarter on quarter, in the second quarter of this year. Growth has been supported mainly by domestic demand, but also reflects some temporary factors. Recent statistical releases and survey evidence generally confirm our expectation of a moderation in the second half of this year in the euro area. Therefore, we do not declare victory and we have to remain cautious and prudent. That being said, the positive but modest underlying momentum of the recovery remains in place. Annual inflation in the
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".