Systemic diagrams to Overcome Setbacks in African Socio-Economic Development
Bibliographic record
Abstract
Abstract Governance inevitably impacts world economy with high socio-economic costs. In the early days after Africans independence there were regional development plans which helped Africa to record high economic growth of 6–7%. However, Africa has never been left to rest after independence, it has been subjugated by the so call advocators which are in control of it resources which in turn influence its socio-economic development. Any time problems of Africans are diagnosed and antidote is administered, a new wound appears. Therefore this paper adopts systemic approach to intervention to identify the setbacks in African socio-economic development. The combination of historic data obtained from some African countries, interviews in Ghana and the literature review regarding the use of the four levels of thinking model provided an overview of the current structures that affect African developing system riddled with feedback loops. Results indicated that aid in reality is not coming to Africa but from Africa to the western world. Developed rich countries donations of 0.7% of their gross national income to support African socio-economic development has not yielded it intended benefits yet since the 70s. Africans are globalized in the contest of opening their markets, under an unjust trade rules leading to the collapse of domestic industries which in turn keeps Africans dependent on imports. This leads to monies being repatriated out of African economies leaving it poorer. African countries have the highest tax rates in the world in order to generate money to pay their loans making it impossible to build roads, factories, hospitals etc in this countries. The world is bothered by Africa when they want to take resources out of Africa. The time is now to wake up and to begin to find African solutions to African problems. Systems thinking to intervention can Africa realized all unintended consequences of her decisions and help it find new ways to improve efficiency and resource economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.023 |
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; both teacher heads agree on what is shown here.
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".