Jean-Pierre Landau: Rebalancing the world economy – a common challenge Keynote address by Mr Jean-Pierre Landau, Second Deputy Governor of the Bank of France,
Bibliographic record
Abstract
references) among others. All interpretations and errors are mine. Great changes are taking place in the world economy. The center of gravity is moving to Asia and the emerging world. This has been recognized in the landmark agreement on IMF reform reached at the last G20 Ministerial meeting. China has become the IMF’s third shareholder, India has moved up by five ranks and Brazil is now on par with Canada (a G7 country). Symmetrically, the European representation in the Board will be reduced by the equivalent of two chairs (out of nine currently held). At the same time, we are facing great challenges. Output is growing fast in emerging economies but this relative shift in production has not been fully matched by a rebalancing in demand. Overall, beyond the turbulences caused by “hot money”, net capital flows are going “uphill ” from emerging to developed economies. That means that some of the poorest citizens of the world are lending money to some of the richest, allowing those to finance their consumption. And financial bubbles have tended to proliferate in an environment of permanently low inflation and ample liquidity. I will argue that those phenomenons can all be ascribed to two common causes: a worldwide
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".