INDONESIA'S MANUFACTURING PERFORMANCE AT THE BEGINNING OF COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Purpose - This study aims to analyze the performance of the manufacturing industry and how the influence of investment and inflation on that performance in the early days of the COVID-19 pandemic. Design/methodology/approach –The performance of the non-oil and gas processing industry is analyzed using the Gross Domestic Product (GDP) of the non-oil and gas processing industry sector. Investment will be reviewed from Foreign Investment and Domestic Investment. Variable Inflation is used as an indicator of price fluctuations in the Indonesian economy. Findings - The analysis reveals the performance of the manufacturing industry, three priority issues: 1. Growth Rate of Non-Oil and Gas Processing Industry Sub-Sector at the start of the COVID-19 pandemic, 2. Realization of Foreign Investment (PMA) and Domestic Investment (PMDN) in the Non-Oil and Gas Processing Industry Sub-Sector, 3. Inflation in Indonesia. Then, how the influence of investment and inflation on that performance in the early days of the COVID-19 pandemic. The priority solution in this finding is to improve the profession, academics, and supporting institutions. Research limitations/implications - This study only examines the problems of 12 sub-sectors of the processing industry at the beginning of the COVID-19 pandemic from the first quarter of 2019 to the third quarter of 2021. This study contributes to the literature by exploring problems and discussing solutions. Practical implications – This study highlights priority issues and discusses solutions to serve as a reference in making policies and strategies for the development of the manufacturing industry in Indonesia. Originality/value – Although there have been several studies in this area, this research is a novelty in examining the problems of the processing industry at the start of the COVID-19 pandemic.
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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 | 0.001 |
| 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) | 0.006 | 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".