COVID-19 economic crisis:Europe needs more than one instrument
Bibliographic record
Abstract
There are now several proposals for complementing the vigorous decision of the ECB to launch a mega ‘pandemic emergency purchase programme’ with fiscal and financial initiatives at the European level. These proposals sometimes overlap, which is a good sign of convergence. This column argues that they are also largely complementary to one another. Hence, it calls for a multi-instrument approach that would jointly achieve three objectives: sharing the cost of the COVID crisis, helping member states to borrow at very long maturities and low interest rates, and relaunching the EU after the crisis. In addition to existing tools, the authors believe that a tryptych built around a COVID fund (with borrowing capacity), specific credit guarantees with the European Investment Bank and dedicated credit lines such as an ESM COVID line or the recently proposed temporary Support to mitigate Unemployment Risks in an Emergency (SURE) would be appropriate, provided it is sized up and allows for very long-run borrowing.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".