Bibliographic record
Abstract
The purpose of the Africa Emerging Markets Forum is to discuss the continent’s economic and social challenges and to share ideas for better results on the ground. This article focuses on the performance and potential contribution of agricultural development, first looking at the current constraints and then setting out some suggestions for the way forward. Over the past 50 years, the normal structural decline in the share of agriculture in the economy and accompanying convergence of incomes in the agricultural and non-agricultural sectors, has not yet happened in sub-Saharan Africa. The economy, in terms of sector shares in total output, has been practically frozen, as has the structure of production within agriculture itself, its technology, and its mode of growth primarily via area expansion. As a consequence, African agriculture remains extremely under-capitalized, and the number of poor and hungry has increased in both the rural and urban areas. Encouraging signs for a new beginning for agriculture discussed in this article include the resumption of economic growth, the reduction in agricultural dis-protection, the end of the secular downward trend in agricultural prices, growing domestic and regional demand for food, improvements in the institutional environment for rural development, and a growing commitment of African Governments to agricultural development. While there is much talk about another structural transformation in Africa toward large scale commercial farming, there appear to be diseconomies of scale in farming, while the success rate of large scale farming has been very limited in Africa. The family farm model therefore remains an appropriate model for most of African agricultural development. To seize opportunities underlying the above-mentioned encouraging signs, sub-Saharan Africa will need to support economic growth by (a) continued sound macroeconomic policies, (b) removing of the remain-ing agricultural taxation that still disadvantages African farmers relative to all other farmers in the world, (c) improving services for small farmers, (d) significantly increasing investment in agricultural technology and its dissemination, (e) empowering local governments, communities, and farmer organizations for their own development, and (f) strengthening the already existing regional agricultural institutions. An overall conclusion emanating from this article is for individual countries to adapt and customize the above broad goals into country-specific action plans to enhance the performance and contribution of the agriculture sector, in line with the CAADP (Comprehensive Africa Agriculture Development Programme) compacts on which they are already working.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".