Bibliographic record
Abstract
This study investigates the relationship between S&P Global Dividend Opportunities Index and market indices of the largest economies of the world, which included U.S., U.K., Germany, Sweden, Spain, Brazil, Hong Kong, Australia, Norway, and Canada for the time period of five years, starting from April 2007 and ending April 2012. Multiple correlation coefficient and coefficient of determination were calculated to study this relationship. The multiple correlation coefficient measured the relationship between the S&P Global Dividend Opportunities Index and market indices whereas the coefficient of determination indicated the percentage of the variation in the S&P Global Dividend Opportunities Index that can be explained and accounted for by the market indices in the regression equation. The multiple regression analysis was performed to study the effect of ten market indices on the movement of S&P Global Dividend Opportunities Index. Results implied that market indices of five out of ten economies when used together better predicted the movements in the S&P Global Dividend Opportunities Index. It was also found that, when individual market Indices were regressed with S&P Global Dividend Opportunities Index, the S&P Global Dividend Opportunities Index was highly correlated with Australia’s market index. The regression equations indicated that the market indices of all economies had positive coefficients in the regression equations, indicating that market indices moved in the same direction as that of the S&P Global Dividend Opportunities Index. TOPICS:Mutual funds/passive investing/indexing, global, performance measurement
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".