BRIC: an integrated group financially?
Bibliographic record
Abstract
This work analyzes the level of financial integration of an economic bloc entitled, on\nan ad hoc way, BRIC, composed by emerging economies with common and growth\npatterns, where more than 40% of the population live in one quarter of the worldâs\nterritory. Following methodologically Vahid and Engle (1993), the results suggest that\nfinancial markets are determined by domestic economic fundamentals in periods of\nglobal economic stability, while in crisis periods, the cycles have greater importance\nin the composition of the returns of the indices analyzed, indicating a higher influence\nof financial risk. The individual cycles, as well as the individual trends are robustly\ncorrelated. These evidences are not trivial since Brazil is a market economy, with\nhigh level of inequality, poverty, democracy and urbanization, Russia is a an exsuperpower\nsocialist, with high per capita income and human capital levels, India is a\nrural society with strong cultural and religious aspects, while China is a communist\ndictatorship with a high degree of trade openness and high levels of international\nreserves. The Indian financial market, which has been undergoing reforms since\n1991, is such that the SENSEX-30 index plays important role in terms of predictability\nof others, as well as its tendency is the only individual to be significant in the exercise\nof causality Granger in the first common trend, the unique related to a promising\nscenario.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".