On Development Strategies for Improving the Management Level of Chinese Enterprises in Africa: the case of Chinese enterprises in Kenyan
Bibliographic record
Abstract
China has become Africa’s largest trading partner. The level of economic and trade cooperation between China and Kenya have been continuously improved within the framework of the China-Africa Community of Shared Future and the “Belt and Road” cooperation. Nowadays, the cooperation between China and Kenya is standing at a new starting point and facing new development opportunities. Chinese enterprises in Kenya have developed rapidly in terms of number and scale in recent years. And the businesses involve a wide range of fields, ranging from agricultural and sideline products and food industry to precision parts processing and manufacturing, which have created a considerable number of jobs for the local area and increased the overall labor income. However, there are still many outstanding problems in specific cooperation practices, such as the lack of attention on corporate management and corporate culture. Based on literature analysis, this article uses Chinese enterprises in Kenya as an example to illustrate the development status of Chinese companies in Africa, study the problems that exist in the development of Chinese companies in Kenya and propose solutions to the corresponding problems. The further development of Chinese enterprises in Africa will promote the better realization of the China-Africa community with a shared future and the development of the “Belt and Road” to achieve a win-win situation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".