Bibliographic record
Abstract
This 2023 Article IV Consultation highlights that following the pandemic, Sweden’s economy experienced a strong recovery, which continued well into 2022. Sweden experienced a strong post-pandemic rebound in 2021–22 but is potentially heading into a recession. The fiscal stance is slightly contractionary in 2023, which is appropriately supportive of monetary policy. This stance should, however, be regularly reviewed in line with growth and inflation developments. Automatic stabilizers should be allowed to fully operate and, if needed, discretional expenditures should be adjusted. The rapid and decisive monetary policy response targeted at reigning in record high inflation is well justified. Notwithstanding the uncertain environment, given the priority to reduce inflation, the recent tightening and readiness to tighten further is welcome. Concrete progress in structural reforms is needed. Reforms should aim at addressing long-standing shortcomings in the labor and housing markets, educational gaps, employment of the foreign-born, and the bankruptcy framework. Fulfilling Sweden’s ambitious climate agenda will also require substantial effort and funding.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.388 | 0.383 |
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".