Full employment as a possible objective for EU policy II: Review of some empirical aspects
Bibliographic record
Abstract
A contribution appeared in the previous issue of Panoeconomicus reviewed the theoretical arguments brought by Alain Parguez and Jean Gabriel Bliek in support of their idea of assigning a full employment objective to European economic policies and their coordination (Bliek and Parguez (2007) and Parguez (2007b)). Without pretending at exhaustiveness, this contribution reviews and partly extends the empirical evidence they presented in support of their argument with reference to selected macroeconomic developments in several countries and different historical periods, in particular for the US, Canada, Japan and the EU. It confirms the descriptive power of the circuit and its relevance for the discussion of alternative economic policies, in particular in the field of employment. Together with the previous article, it shows that the circuit can be used to update economic policy thinking, nourishing also the necessary democratic debate amongst police alternatives. .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".