Dampak Pandemi Covid-19 Terhadap Dinamika Ekspor ASEAN 5: Pendekatan Panel Kointegrasi
Bibliographic record
Abstract
This study examine the effect of COVID-19 pandemic on ASEAN5 export performance. The study begins with an exploration of ASEAN5 export data, before and after pandemic. Panel cointegration model (FMOLS and DOLS) is applied to determine the long-term relationship between exports and pandemic. During pandemic, energy exports performance (such as coal, natural gas, and oil) decreased. In general, machineries (HS 84) also experienced a decline in export performance. On the other hand, exports of palm oil and gold were able to grow positively. Exports of the electrical equipment (HS 85) were also able to grow positively for most countries. FMOLS and DOLS models showed a long-term relationship between exports and the pandemic. Furthermore, pandemic and real exchange rate have a negative relationship with exports while economic liquidity (M2) has a positive effect on exports.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".