Long-term economic outlook for Japan, as impacted by COVID-19
Bibliographic record
Abstract
Abstract Due to COVID-19, Japan’s GDP decreased by 4.5% in 2020 from 0.7% in 2019. The economy increased by 1.7% in 2021, stagnated at 1.4% in 2022, and is expected to grow at 1.8% in 2023 and to slowdown to 0.9% in 2024, based on the January 2023 forecasts of the International Monetary Fund (IMF). IMF’s January 2023 report is based on inflation peaking with low growth due to rising interest rates. In January 2021, a year into the COVID-19 pandemic period, the IMF was hopeful, predicting a V-shaped growth pattern of 3.1% for 2021 and 2.4% for 2022 due to policy stimulus and the availability of vaccines. However, this did not materialize due to various geopolitical and economic shocks. The economic costs of the COVID-19 pandemic relative to its absence are estimated to be at least US$1.1 trillion (¥160 trillion) until 2030 under a continued low economic growth future path. Moreover, the estimated US$1.1 trillion economic loss is equivalent to the erasure of approximately 30% of GDP produced in 2019 during the Abenomics era. If in the absence of the pandemic, the economy was assumed to have a high growth, the losses would reach US$ 4.8 trillion (¥706 trillion) due to the lost opportunity of a high-growth counterfactual trajectory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".