Bibliographic record
Abstract
A report on investment trends in the BRICS economies since the creation of the grouping, and a discussion of possible future paths for collaboration in the area of investment. Brazil, the Russian Federation, India, China and South Africa (BRICS) now form one of the world’s most important economic blocs, representing more than one quarter of global GDP, and 42 per cent of the world’s population. Significantly, the BRICS have seen their economic influence increase over the past decades, as drivers of global growth, trade and investment. Since Jim O’Neil created the acronym BRIC, in 2001, the grouping has both expanded, and deepened its collaboration. In 2011, South Africa joined, to create the BRICS economies. Although the bloc is an informal arrangement, with no charter, it has nonetheless developed a more institutional character, both through a high level of political interaction (e.g. annual summits) and the creation of economic institutions such as the New Development Bank (NDB) and the Contingent Reserve Arrangement (CRA). Foreign investment has played an important role in the growth of BRICS economies since 2001, with annual FDI inflows to the bloc more than quadrupling from 2001 to 2021 and contributing significantly to gross fixed capital formation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.254 | 0.197 |
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".