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Record W2770533460

Youth Employment in Sub-Saharan Africa [L’emploi des jeunes en Afrique subsaharienne - Rapport complet]

2014· article· fr· W2770533460 on OpenAlexaboutno aff
Deon Filmer, Louise Fox

Bibliographic record

VenueWorld Bank Publications · 2014
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNonfarm payrollsGross domestic productAsset (computer security)ProductivityQuarter (Canadian coin)AgricultureEconomic growthDevelopment economicsPolitical scienceEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.059
GPT teacher head0.234
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2014
Admission routes1
Has abstractyes

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