Bibliographic record
Abstract
Introduction concept of the as it relates to the emigration of educated or economic elites of a nation has been studied since the period just before, during and after World War II, when highly educated Europeans fled Europe to North America and other parts of the developed world. According to Tucho (2009): The term Brain often known as human capital flight, is a nearly half-century-old phenomenon by which highly skilled professionals and academics seek higher paying job opportunities in other countries was in the 1950s when there was an attempt to halt the exodus of highly skilled and educated Britons to North America, particularly the United States, for better job opportunities that the term Brain Drain was used for the first time (p.23). According to Vidysagar (2006): It was the British Royal Society that coined the expression 'Brain Drain' to describe the outflow of scientists and technologists from UK to US and (p.246). This paper examines Africa's emigration Drain, especially focusing on developed or rich countries. paper begins with a diagnosis, by presenting various types of statistics showing the numbers and percentages of African immigrants in developed countries. also presents any information regarding the progress of these African immigrants in those developed nations, such as household incomes, salaries, educational attainment, and any contributions to their host countries. paper goes on to present information pertaining to the implications or consequences to Africa or Africans in Africa as a result of the brain drain to developed countries. This paper continues by presenting the factors or causes for Africa's emigration brain drain. paper also presents information showing any types of benefits to Africa or Africans in Africa as a result of the continent's emigration brain drain to the West or developed countries. Finally, the paper presents some suggestions or recommendations as to how Africans both at home and abroad and those countries and organizations concerned with this phenomenon could manage it properly. Let us now begin by going over various statistics of the numbers and percentages of Africans in the West or developed nations and their progress and contributions to those host countries. Numbers and Percentages of African Immigrants in Developed Countries There have been massive numbers of African immigrants who have left Africa for developed countries in the post World War II era, especially from the 1990s to present. Today, millions of African immigrants are residing and attending colleges and universities, working or running their own businesses in developed countries. According to Lindley (2008) Sixteen million international migrants originate from sub-Saharan Africa ... (p. 1). Vidysagar (2006) points out that: It is estimated that there are 10 million African-born emigrants living in US, UK, and other countries outside of Africa (p.246). According to the U.S. Census Bureau, as of 2007, there were 1.419 million African immigrants in the United States (Table 44. Foreign-Born Population, 2010). According to Statistics Canada (2007), as of 2006, there were 374,565 African immigrants in Canada (Immigrant Population by Place of Birth, 2007). According to the UK Office for National Statistics, the total population of the UK in 2008 was 61.4 million. Of that total, excluding other people of Black African decent, Black Africans accounted for 1.4% (860,000) and people who are grouped as White and Black African accounted for 0.2% (123,000) (Population Trends, 2009, pp.7 & 9). As of 2002, an estimated 2.46 million sub-Saharan African immigrants were in 20 European nations (Compiled and computed from Bail, 2008, p.41). In Australia, according to the Australia Bureau of Statistics (2008, August 20): ...there were 248,699 people born in Africa who were resident in Australia in 2006. …
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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 | 0.003 | 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 | 0.001 |
| 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".