UNEMPLOYMENT DYNAMICS IN THE ASIA-PACIFIC REGION: A PRELIMINARY INVESTIGATION
Bibliographic record
Abstract
This study empirically examined unemployment dynamics in 12 countries in the Asia-Pacific region, namely, China, Hong Kong, Taiwan, South Korea, Japan, Indonesia, Malaysia, the Philippines, Singapore, Thailand, Australia and New Zealand. It used quarterly data on the unemployment rates from the first quarter of 1980 to the first quarter of 2013. This paper employed three different econometric methods, including the recently-developed powerful unit root test with structural break (Lee and Strazicich, 2003, 2004) and the nonlinear unit root test (Enders and Lee, 2012). The findings indicated that the unemployment rates in five countries of the region, namely, China, Taiwan, South Korea, the Philippines and Thailand, had highly dynamic labor markets in which higher-than-normal unemployment rates would revert to the normal level. The other seven Asia-Pacific countries had less dynamic labor markets. The findings of this study have some important policy implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".