Doing Business in Africa: Some Challenges
Bibliographic record
Abstract
In 2000, The Economist depicted Africa as “the hopeless continent” where “wars still rage from north to south and east to west” and countries are deluged by government-sponsored thuggery, fl oods, famine, poverty, diseases and pestilence ( The Economist , 2000). Such depictions have fuelled pessimism about the attractiveness and effi ciency of the Africa’s business environment among businesses and policy makers. Other regions like Asia and Latin America have on the other hand received endorsements as favourable places for doing business during the same period. A number of scholars have tabled additional reasons in support of this Afropessimism. Asiedu (2003) and Ajayi (2006), for instance, suggest that issues with governance failures, macroeconomic instability or policy failures, problems of policy credibility, poor liberalization policies, political instability, corruption, poor infrastructure, infl ation and investment restrictions are some of the reasons accounting for the negative image of the continent. Others mention diseases, natural disasters, military coup d’états and wars as part of Africa’s problem (Cleeve, 2009). Rogoff and Reinhart (2003) showed that during the 19602001 period, 40 per cent of the countries in Africa have had at least one war, and 28 per cent had two or more wars. Musila and Sigué (2006) noted that this rate is three times more than that in the western hemisphere (excluding Canada and US), and twice that of Asia. These evidences and depictions have been fuelling Afro-pessimism within the international business community.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".