DAMPAK BADAI START-UP DI INDONESIA PADA SAAT MASA PASCA PANDEMI COVID-19 TERHADAP HARGA SAHAM PERUSAHAAN E-COMMERCE
Bibliographic record
Abstract
Several e-commerce start-ups in Indonesia such as PT.Bukalapak.com Tbk. (BUKA), PT. GoTo Gojek Tokopedia.Tbk. (GOTO) and PT. Global Digital Niaga.Tbk. (BELI), which is a national company in Indonesia, has become a savior for MSMEs where when MSMEs are prohibited from opening their businesses, MSMEs can sell online so they can still trade even from home. This e-commerce is very helpful for MSME players so that their business turnover can continue even though maybe not up to 100 percent like before the pandemic but at least they can still circulate money for their business so they don't go bankrupt and there are even some MSMEs whose online business is through e-commerce is very advanced beyond its offline store turnover. In the third quarter of the 2022 period where since the beginning of 2022 in the world there has been a war between Russia and Ukraine so that this war has affected the economies of many countries so that many countries have been entangled in inflation so that many World Central Banks have raised their interest rates. The problems that occurred besides the declining stock prices due to the increase in deposit rates, the problem that occurred was that many e-commerce companies were starting to run out of money to carry out promotions which are commonly called cash burn, where the money injected by investors, namely venture capitalists and angel investors, starts to run out and e-commerce companies have to start thinking about reducing their promotions and starting to make a profit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".