Bibliographic record
Abstract
The purpose of this study is to determine the factors that influence currency fluctuation in Malaysia. Since this currency in Malaysia becomes a serious issue in economic condition, as a researcher has made about Malaysia Ringgit with the US Dollar day by day. In the third Quarter in year 2016, the exchange rate is RM4.3742 to 1USD. It means that MYR is in decreasing value which is the lowest amount within 17 years since Asian crisis. It is also can be related to the bad financial condition in Malaysia. Different analyst has different opinions. Some have said the currency fluctuate is because global oil prices had been falling and some said because the market of China has been in decline. China is the second largest economic power after the US in the world. The variables that being used in this study are inflation rate and interest rate as independent variables while currency in Malaysia as dependent variable. This study used descriptive statistic, unit root test, regression using panel data and diagnostic test in order to test the hypothesis. The data is collected from first Quarter in year 2009 until second Quarter in year 2017. All these three variables show positive and significant effect towards currency in Malaysia. It means that all these variables are the factors that influence currency fluctuation in Malaysia. But, in diagnostic test there is autocorrelation problem. It means that the result from these findings might not be reliable to use in another period. This study is important since it will give knowledge towards the government, banking sector in order to make the decision on stability of the Malaysia Ringgit for a good economic Malaysia.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".