Global Outlook for 2018: Economy, Finance, and Monetary, with a Particular Case Study of Taiwan
Bibliographic record
Abstract
In October 2017, IMF President Christine Lagarde declared that the GDP growth of world’s economies in the first half of 2017 was up to the broadest recovery since 2010. So far, the strength of global economic growth has been enhancing. The interest rates and inflation are still at a low level. The global economy has risen from the bottom in 2016 to reach its peak since 2011. As for the degree of economic development, the emerging markets grew fastest, followed by the developing countries, while the advanced economies grew moderately at an average rate around 2%. Manufacturing PMI in major countries, such as the United States, China, the Eurozone, and even Taiwan, have increased above 50 notably in the recent years, while the non-manufacturing PMI is also above 50. Accordingly, the main purpose of this paper is to forecast the global economy in 2018, which is on the trajectory of booming with a certain degree of uncertainty. A particular case study of Taiwan’s overall economic development is presented as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".