Determinan Nilai Perusahaan Sebelum dan pada Masa Pandemic Covid-19
Bibliographic record
Abstract
This study aims to see the condition of companies in the consumer goods industry sector before the pandemic and during the pandemic using indicators such as Current Ratio, Debt to Equity Ratio (DER), Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin ( NPM) and company size against firm value. In the period before and during the Covid-19 PandemicThe method used in this research is quantitative method using secondary data from the company's financial statements for the first quarter until third quarter of 2019 and 2020 using regression method and different test Paired Sample t Test. . The results of this study indicate that (1) the Current Ratio before the Covid-19 pandemic had a significant negative effect on firm value, while Current The ratio during the Covid-19 pandemic did not have a significant effect on the firm value variable, (2) the Debt to Equity Ratio before the Covid-19 pandemic had a significant negative effect on the firm value variable, while the Debt to Equity Ratio during the Covid-19 pandemic had no significant effect on company value, (3) Return on Assets before and during the Covid-19 pandemic did not have a significant effect on the firm value variable, (4) Return on Equity before and during the Covid-19 pandemic had no significant effect on firm value, (5) Net Profit margin has a significant negative effect on firm value before and during pandemi Covid-19, and (6) company size has a significant positive effect on company value before and during the Covid-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".