Bibliographic record
Abstract
Target2 is the Eurozone’s cross-border payment system, which is mandatory for the settlement of euro transactions involving Eurozone central banks. It is being used to save the Eurozone from imploding. A key underlying problem is that the Eurozone does not satisfy the economic conditions for being an Optimal Currency Area, i.e., a geographical area over which a single currency and monetary policy can operate on a sustainable, long-term basis. The different business cycles in the Eurozone, combined with poor labour and capital market flexibility, mean that systematic trade surpluses and deficits will build up because inter-regional exchange rates can no longer be changed. Surplus regions need to recycle the surpluses back into deficit regions via transfers to keep the Eurozone economies in balance. But the largest surplus country—Germany—refuses to formally accept that the European Union is a ‘transfer union’. However, deficit countries, including the largest of these—Italy—are using Target2 for this purpose. Target2 has become a giant credit card for Eurozone members that import more than they export to other members, but with two differences compared with normal credit card debt: neither the debt nor the interest that accrues on the debt ever needs to be repaid. Furthermore, the size of the deficits being built up is causing citizens in deficit countries to lose confidence in their banking systems, leading them to transfer their funds to banks in surplus countries. Target2 is also being used to facilitate this capital flight. However, these are not viable long-term solutions to systemic Eurozone trade imbalances and weakening national banking systems. There are only two realistic outcomes. The first is a full fiscal and political union, with Brussels determining the levels of tax and public expenditure in each member state—which has long been the objective of Europe’s political establishment. The second outcome is that the Eurozone breaks up.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".