Bibliographic record
Abstract
Mindless austerity is losing policy credibility in some Eurozone nations.This column suggests governments shouldn't mix long-term growth and fiscal discipline nor produce another Lisbon strategy.Instead, they should adopt a framework for fiscal policy cooperation, restructure debts, and remember that fiscal discipline is for the long run.It is not just the election of François Hollande in France.Adopting contractionary fiscal policies in the teeth of a double-dip recession never made sense.And yet, public debts are high and markets in endemic panic.The solution must be based on a comprehensive analysis of the situation, not on arcane debates on the strength of the "confidence factor".It ought to combine debt restructuring, front-loaded collective fiscal expansion and long-run unbreakable commitments to fiscal discipline.The next French president is mistaken when he mentions in the same breath the Fiscal Compact and the growth objective, but he has hit raw nerves.Under German pressure, his predecessor has endorsed the ideological view that governments under market pressure for their excessive debts ought to atone and close the deficit, even if this means procyclicality.Strangely, the Commission, highly vocal against procyclicality in upswings, supports the German view.Under pressure from its friends, Greece has been forced to implement this policy for two years now and the results are plain to see.The debt ratio keeps growing.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".