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Record W4385895684 · doi:10.1108/oxan-db281194

Hungary will struggle to mend its fiscal woes

2023· article· en· W4385895684 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyDeficit spendingInflation (cosmology)EconomicsProfit (economics)Quarter (Canadian coin)Government (linguistics)Fiscal yearFiscal deficitOrder (exchange)Economic policyFiscal policyFinanceMacroeconomicsMarket economyHistory

Abstract

fetched live from OpenAlex

Significance This is the lowest monthly widening since the start of 2023. Hungary aims to curb its 3.9% of GDP budget deficit in order to avoid being placed under the EU’s Excessive Deficit Procedure. It has also seen inflation dropping from 20.8% year-on-year in June to 17.6% in July. Impacts Finance Minister Mihaly Varga is openly talking about revising the 2023 budget in September. Hungarians see high inflation as the main problem, as this has caused a rapid decline in real wages. The government will be reluctant to phase out politically sensitive housing subsidies in full. Hungarian companies’ profit margins -- measured by operating surplus divided by real GDP -- have grown by 61% in the first quarter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0160.007

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.

Opus teacher head0.046
GPT teacher head0.348
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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