Youth Employment in Sub-Saharan Africa [L’emploi des jeunes en Afrique subsaharienne - Rapport complet]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sub-Saharan Africa has just experienced one of the best decades of growth since the 1960s. Between 2000 and 2012, gross domestic product (GDP) grew more than 4.5 percent a year on average, compared to around 2 percent in the prior 20 years (World Bank various years). In 2012, the region's GDP growth was estimated at 4.7 percent- 5.8 percent if South Africa is excluded (World Bank 2013). About one-quarter of countries in the region grew at 7 percent or better, and several African countries are among the fastest growing in the world. Medium-term growth prospects remain strong and should be supported by a rebounding global economy. The challenge of youth employment in Africa may appear daunting, yet Africa's vibrant youth represent an enormous opportunity, particularly now, when populations in much of the world are aging rapidly. Youth not only need jobs, but also create them. Africa's growing labor force can be an asset in the global marketplace. Realizing this brighter vision for Africa's future, however, will require a clearer understanding of how to benefit from this asset. Meeting the youth employment challenge in all its dimensions, demographic, economic, and social, and understanding the forces that created the challenge, can open potential pathways toward a better life for young people and better prospects for the countries where they live. The report examines obstacles faced by households and firms in meeting the youth employment challenge. It focuses primarily on productivity, in agriculture, in nonfarm household enterprises (HEs), and in the modern wage sector, because productivity is the key to higher earnings as well as to more stable, less vulnerable, livelihoods. To respond to the policy makers' dilemma, the report identifies specific areas where government intervention can reduce those obstacles to productivity for households and firms, leading to brighter employment prospects for youth, their parents, and their own children.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it