Bibliographic record
Abstract
In the last two decades China’s economic influence in Africa has increased espoused by huge investment in infrastructure like roads, railways, airports and seaports. This has led many scholars to suggest that Africa is facing East away from the traditional West. The Western influence had permeated into governance, education religion and even consumption. Of interest is if China has successfully displaced the west from Africa in such a short time. This study investigates if Africa, in particular Kenya has really faced East (read China). We expect economies near each other geographically or are culturally close because of history e.g. colonialism to have highly correlated GDP growths. This is supported by gravity theory of trade. In this paper, GDP growth rates of Kenya and a selected number of countries from the West and East are correlated for a 50 years period. Analysis is then broken into decades to see the change in patterns. Analysis of correlations during the different Kenyan presidencies then before and after the cold war is carried out. All the data in this paper is sourced from World Development Indicators, a World Bank Data base. The hype about facing East for Kenya is not supported by data. Kenya in the last 20 years has looked East, but did not abandon the West. This dualism may change with Brexit, Trump in White House and envisaged Africa’s free trade area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".