Private Sector Development Interventions and Better-Quality Job Creation for Youth in Africa
Bibliographic record
Abstract
There is consensus among policymakers and the research community that demand for young people’s labour is the main constraint to achieving sustainable job creation in Africa (e.g. Fox & Kaul, 2017; AfDb et al., 2012). Thus, this synthesis paper focuses on evidence around the creation of wage labour opportunities in the private sector, linking youth employment with a desired structural economic transformation able to absorb the predicted surge in supply of labour in the decades to come. African economies have failed to transform structurally from low productivity agriculture to higher productivity non-agricultural sectors, with recent economic growth based on commodity exports not delivering enough jobs and lacking inclusive and sustainable linkages with local businesses to increase productivity at enterprise and sector level. Without sufficient policies in place to improve productivity at firm and sector level, the “extremely unproductive” informal sector, with its typically poor-quality employment conditions, will remain a major employer for youth, particularly the less skilled and educated. This synthesis paper looks at what is needed for the private sector in Africa to create more sustainable and quality jobs for youth working in the formal and informal sectors, by linking labour market outcomes with enterprise development interventions.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".