Hungarian economic stimulus measures will leave gaps
Bibliographic record
Abstract
Subject The series of tax-related measures that the Fidesz government hopes will boost competitiveness and support GDP by reducing labour shortages. Significance Following disappointing economic growth of just 2.2% on an unadjusted basis in the third quarter, owing to a larger-than-expected drop in investment, Fidesz’s latest tax-related measures are well-timed, since the economy is expected to slow in the final quarter of 2016. The government insists no amendments will be needed in the state budget, and is now forecasting 3.1% GDP growth in 2017, after 2.5% this year. Impacts Value-added tax cuts and rises in public-sector minimum wages will cause inflation to rise faster in 2017, as deflationary trends disappear. The unemployment rate is expected to bottom out as workers return from neighbouring countries. The government will need to make complementary reforms in education and privatising the state-dominated energy and telecoms sectors. If it does not, competitiveness as measured by wage growth and productivity will remain subdued.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.131 | 0.071 |
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".