The factors that affecting the price of gold / Nurulfazura Aisyah Ahmad Arsani
Bibliographic record
Abstract
According to Theloosen (n.d.), even though the gold has attracts the interest of the investor, the factors that drives the price of gold is still not completely known. There is still no valid factors that gives explanation and details on how these economic variables affect the gold price. For this reason, the research is done to identify how inflation rate, interest rate, gross domestic product and crude oil price affect the price of gold. This research use secondary data provide from Index Mundi and World Data. It provides the data from all over the world since the research involves eight countries which is Australia, Russia, Unites States, Canada, Mexico, Brazil, Indonesia and Chile. The data is using 80 observations annually from year 2006 to 2015 which is 10 years. The variables use in this research is dependent and independent variables. Price of gold is a base or dependent variable and inflation rate, interest rate, gross domestic product and crude oil price are independent variables. The data has been analysed by using Eviews 8.0 to do descriptive, unit root test, multiple regression, correlation analysis and test on assumption.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".