Bibliographic record
Abstract
China’s emergence as the would-be largest economy despite practising Communist regime, not western-style democracy has come to many people as “taken aback”. China could emerge ahead of all the Asian Giants: Japan, Korea, Singapore etc. There is no definite consensus among financial matters analysts and scholars about the role of that era in the outcomes observed today: while some believe that the ‘foundation’ for the recent performance was laid during the communist era, others see those decades as lost decades for China. This paper will explore avenues whether African nation is the “Next Wonder” like China in the global economic growth story to emerge from a near basket case to become the largest economy in the world by 2050 ahead of Korea, Italy, Canada, etc? The issue therefore is not whether Africa has the potentials to be the ‘next surprise’ as China, The key area of emphasis is building and exploiting the networks of the African elites in Diaspora. A careful disaggregation of the ethnic origins of Foreign Direct Investment flows might show that Asian-Japanese, Chinese, etc., still invest mostly in Asian countries; while Americans and Europeans mostly invest among themselves. It is not surprising that African countries receive much less FDI than would be predicted by the fundamentals of their economy, whereas other countries in other regions receive much more even when they reform less. It is estimated that about 50 percent of the FDI into China in recent decades came mostly from the ethnic Chinese in Diaspora. In the same vein, Israel is said to receive most of its FDI and assistance from Jews all over the world. The ethnic Diaspora not only remits money remittances and FDI, but also provides a veritable source of skilled manpower and technology transfer. They also provide the ready networks for opening markets for trade abroad. It may not be inappropriate to also surmise that at least 40 percent of Africa’s most talented and skilled manpower reside outside of the region the brain drain. Nigeria alone is said to have about 17 million elites abroad of which over 10,000 medical doctors live in the USA. The real tragedy is that Africa runs the risk of being perhaps the only region of the world that may not benefit over time from the continued growth and development impact of the Diaspora. For example, wherever you go, ethnic Chinese, still speak their native language even after several generations of settlement in the place. Indeed, the China immigrants here in Malaysia have forced them to literally adopt Chinese language as its medium of instruction in Chinese schools. In the case of Africa, the pressure to adapt and be ‘accepted’ in the West forces them into extreme forms of self- rejection brain draining. The impact of this phenomenon is that the children and perhaps future generations of the current generation of Diaspora would not be able to speak the language of their parents, would not know much about the culture and traditions, and hence would never have any attachments to Africa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".