Bibliographic record
Abstract
It has been more than 23 years since Jim O’Neil, head of economic research at the investment bank Goldman Sachs (GS), wrote in an internal policy paper that four countries, Brazil, Russia, India, and China (BRIC). Seeking to attract investors, in his 2001 GS Global Economic Paper No. 66, “Building Better Global Economic BRICs,” O’Neil focused on investment opportunities in four developing countries— Brazil, Russia, India, and China (BRIC), O’Neill further argued that the balance of world economic powers was already tilting in favor of these four countries which he labeled as “BRIC” economies. The BRIC nations embraced the term and invited South Africa In 2010 to join them. Hence the acronym BRIC became “BRICS.” In this paper, I plan to focus on economic progress of BRICS countries, as reflected in their GDP, international trade, and Foreign Direct Investment (FDI). I will illustrate the power of BRICS as well as the global challenges these countries will face in the coming decades in achieving their mission to change the global order.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".