Bibliographic record
Abstract
In 2015, Goldman Sachs closed its BRIC (Brazil, Russia, India, China) fund after years of losses and plummeting assets. Emerging markets had, once again, turned into submerging markets. Their dependence on “developed” markets and established institutions had failed them in a post-Global Financial Crisis (GFC) era, anchored in protectionism, risks, volatility, and uncertainty. The once commonly-accepted wisdom that called for US housing prices to always increase was part of the problem and contagion. Rebuilding the BRICS (S for South Africa) using conventional wisdom would probably not work. A new approach is necessary, especially since the last key contributions to show the inadequacy of a conventional wisdom-based strategy in emerging markets are more than ten years old. To help fill this gap, this paper proposes a holistic analytical framework for strategists to re-assess risks and opportunities in the BRICS. We illustrate how five basic assumptions can be proven wrong and lead to the creation of unconventional wisdom that can help derive some strategic insights. We find that rebuilding the BRICS for them to be more resilient is possible, if not vital, for the health of the global economy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".