The Donkey and the Thorn Tree: Reappraising Globalisation and Africa
Bibliographic record
Abstract
Africa is vulnerable to a perfect storm which comprises a burgeoning youthful population, insufficient infrastructure, benign donor neglect and more malign foreign interference, much of which can be traced to decades of weak economic performance. African excuses for such failure have focused largely on external factors. But countries from similar domestic environments and in the same world order across Asia, Europe and Latin America have developed in leaps and bounds. This would suggest that, for at least some countries, Africa is poor because its leaders have chosen the wrong path. This essay provides a reappraisal of globalisation vis-à-vis Africa, arguing that the continent does not have too much globalisation, but too little in the form of open competition for business and markets, and that politics, not economics, is the principal development impediment. Examples on the continent (Somaliland) and elsewhere (Singapore) illustrate what impact effective politics can have, highlighting that a major challenge for Africa is an inability to create regional exemplars of prosperity that other states can emulate and feed off in a positive cycle of development. To this end, getting the (democratic) politics right in Africa makes good development sense.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".