Bibliographic record
Abstract
The trade forecast for Taiwan conducted by the Chung-Hua Institution for Economic Research (CIER), the Directorate-General of Budget, Accounting and Statistics (DGBAS), and the Institute of Economics, Academia Sinica (lEAS) has received considerable attention from decision makers in the private and public sectors. We evaluate the forecasting performance of the three institutions in terms of the conventional criteria and the usefulness tests recently developed by Lin et al. (2011). More specifically, we analyze the samples for the annual and quarterly projections released by CIER, DGBAS and IEAS from 1996 to 2010. Our findings are as follows. First, the directional accuracy statistics show that the one-year-ahead annual projections are generally well produced. Second, Ashley's usefulness statistics indicate that the current-quarter forecasts released by those institutions perform the best. In addition, based on the tests for usefulness (Lin et al., 2011), the annual forecasts have also done a good job. Overall, the current year forecasts (prepared in the middle of the same year) produced by DGBAS and the next-year forecasts (prepared at the end of each year) produced by IEAS perform the best. Meanwhile, the current-year forecasts for the changes in trade between Taiwan and specific countries produced by CIER also provide useful information.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".