Assessment and recommendations
Bibliographic record
Abstract
While Finland was insulated from the direct effects of the recent global financial crisis due to its prudently managed financial sector, the worldwide recession and collapse in trade hit the country harder than most other OECD countries. Real GDP declined by over 9% from the peak in mid-2008 to the second quarter of 2009, led by declining export volumes which fell by close to one third. This extraordinary collapse in trade can to a large extent be attributed to the composition of Finnish exports, with a high dependence on information and communication technology (ICT) and capital goods, and exceptional exposure to hard hit markets such as Russia. Compared to other OECD economies, exports have also been slow to recover. Fast rising unit labour costs due to high wage increases and an appreciating effective exchange rate have deteriorated competitiveness over the last few years, potentially denting Finland’s export performance. The high wage increases boosted household income and sustained consumption through the downturn, but the negative effects on exports from lower competitiveness can weigh more heavily as the world economy rebounds. While underlying inflation in the past was lower than the euro area average, it has been higher since mid-2008 despite a wide output gap.
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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.267 | 0.122 |
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".