In the Midst of the Coronavirus and Geopolitical Crises—Inventory Efficiency and Challenges Faced in Finland
Bibliographic record
Abstract
Before the COVID-19 pandemic, the world lived through loose monetary policy and low interest rates. These were further reinforced in 2020, and product-based demand increased throughout the world. Due to these, as well as the suddenly developing geopolitical crisis in Ukraine, inflation started to accelerate (both consumer and producer), and this was especially the case in Europe. Therefore, there is a need for descriptive analysis on how trade and manufacturing companies have reacted to the existing multifaced crisis. This research used data of Finnish publicly traded companies. On the basis of the results, inventories increased in the longer term, especially in 2021 and 2022 (the first half of the year). Content analysis revealed reasons for inventory build-up in 2021–2022, with these being the result of many different causes. In some cases, business expansion or decline was said to be the reason, while in others, it was availability issues faced and purchasing price increases experienced. Interestingly, Russia was directly mentioned as a reason by only a few companies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".