Effectiveness Of Mix Police (Fiscal And Monetary Police) In Reducing Unemployment In 5 Southeast Asia Countries (Indonesia, Malaysia, Thailand, Singapore And Philipnes)
Bibliographic record
Abstract
This study aims to analyze the contribution of the effectiveness of the mix police (fiscal and monetary police) in reducing unemployment in 5 Southeast Asian countries (Indonesia, Malaysia, Thailand, Singapore and the Philippines). This study uses secondary data or time series, namely from the first quarter of 2001 to the fourth quarter of 2018. The data analysis model in this study is the Vector Autoregression (VAR) model and the ARDL Panel then sharpened by analysis of Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). The results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the present variable both on itself and on other variables. From the estimation results, it turns out that there is a reciprocal relationship between one variable and the other variables that contribute to each other.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".