Lessons from/for BRICSAM about South-North Relations at the Start of the 21st Century: Economic Size Trumps All Else?
Bibliographic record
Abstract
In the coming decades, China and India will disrupt workforces, industries, companies and markets in ways that we can barely begin to imagine … How these Asian giants integrate with the rest of the world will largely shape the twenty-first century global economy (Business Week 2005:38). As the Davos program illustrates, India, long overshadowed by China … is the country of the moment. Signs abound of an India surging with self-confidence … At the root of this change is a reappraisal of the country's economic potential. This has been brought on by a jump in the trend growth rate to 7–8%, double the “Hindu rate of growth.”… As the balance of power in the global economy shifts towards Asia, such turbocharged growth rates promise to shorten the time-frame in which India rises up the ranks of the world economic powers (Financial Times 2006:1). China and India, 49th and 50th, respectively, ranked much more closely than in previous years. While China dropped three ranks, India moved up five places…. India's improved rank mirrors the country's somewhat higher position in the technology index… Both countries continue to suffer from institutional weaknesses which, unless addressed, are likely to slow down their ascension to the top tier of the most competitive economies in the world (WEF 2005:xv).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".