The Status of Africa’s Emigration Brain Drain in the 21st Century
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».