Modest economic downturn in Austria on the back of a slowing global economy
Bibliographic record
Abstract
Amid weakening global growth, economic activity in Austria has also been slowing down, albeit only moderately thanks to robust domestic demand. Based on the results of its Economic Indicator, the Oesterreichische Nationalbank (OeNB) expects real GDP to expand (quarter on quarter) by 0.2% in the third quarter and by 0.3% in the fourth quarter of 2019. This implies a downward revision of 0.2 percentage points from the OeNB’s Economic Indicator of May 2019. Real GDP is, however, still expected to grow by 1.5% in 2019 as a whole, as real GDP data for the beginning of 2019 have been revised slightly upward. In its most recent inflation forecast of September 2019, the OeNB anticipates HICP inflation to decline from 2.1% in 2018 to 1.6% in 2019, and to remain at this level in both 2020 and 2021. Compared with the OeNB’s June 2019 outlook, this represents downward revisions of 0.1 percentage points for each of the years from 2019 to 2021. In 2019, the decline in inflation has been driven by lower energy price inflation, which is masking persistently high wage pressures and robust domestic demand, both of which are not expected to decrease before 2020. As a result, core inflation (excluding food and energy) is projected to reach 1.8% in 2019 and 2.0% in 2020, before dropping to 1.7% in 2021, given the cyclical downturn in Austria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".