Respond to Covid-19 challenges: Unconstrained growth and policy options
Bibliographic record
Abstract
In the first, empirical part of the paper, we have dealt with the previous recession episodes in Serbia in the 15-year interval from 2006 to 2020 and the direct impact of the Covid-19 crisis. We have compared the long-term and short-term trends and one-off Covid-19 impacts on the real and monetary economy, financial sector, and the rest of the world. Key lessons drawn from the previous crises are highly relevant today. The second part of the paper is analytical. For that purpose, we have updated our DSGE model with the data until the last quarter of 2020 and simulated nine alternative scenarios of fiscal, monetary, and industrial policies over the next five years. They showed remarkable results in some sectors, but created imbalances in others. Focusing on GDP growth in the post-Covid-19 period is misleading since the economy will never be the same. There is a need to choose an optimal mix of conventional policy measures and an industrial policy based on digitalisation and IT. The current Government policy of a huge fiscal deficit and rising public debt exposes the country to unbearable risk in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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 teacher head, 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".