RISK MITIGATION FOR SMALL AND MEDIUM-SIZED ENTERPRISES (SMES) IN THE MIDDLE OF VOLATILITY IN THE WORLD’S ECONOMY CONDITION
Bibliographic record
Abstract
ABSTRACT Indonesia's economic growth in the first quarter of 2020 of 2.97% was released by the Central Statistics Agency (BPS). It is undeniable, that number is the lowest growth rate in the last 19 years. We understand that the economic disruption caused by the COVID-19 pandemic did occur in various parts of the world. A significant economic slowdown is a big task for many national leaders. Some world economic experts even mention that the disruption of the economy due to this pandemic can resemble the effects of the Great Depression of 1930 ago. If we review the impact of the COVID-19 pandemic which has caused extraordinary disruption in the economic field, it is seen that Micro, Small, and Medium Enterprises (MSMEs) are a sector that is quite severe. Basically, the concept of risk management is not commonly used in SMEs business processes. This is because, in general, the resources owned by SMEs are quite limited. However, in this paper I want to illustrate at least there are simple concepts that can be applied by SMEs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".