Digital Networking and the Case of Youth Unemployment in South Africa
Bibliographic record
Abstract
Abstract South Africa has one of the highest rates of youth unemployment and under-employment around the world, despite having a relatively large formal sector. This is driven, in part, by frictions in labor markets, including lack of information about job applicants’ skills, limited access to job training, and employers’ reliance on referrals through professional networks for hiring. This case study explores whether the online platform LinkedIn can be used to improve the employment outcomes of disadvantaged youth in South Africa. Researchers worked with an NGO, the Harambee Youth Employment Accelerator, to develop a training for young workseekers in the use of LinkedIn for job search, applications, and networking for referrals. This intervention was randomized across 30 cohorts of youth, with more than 1600 students enrolled in the study. The research team worked with LinkedIn engineers to access data generated by the platform. The evaluation finds that participants exposed to the LinkedIn training (the “treated” participants) were 10% more likely than the control group to find immediate employment, an effect that persisted for at least a year after job readiness training.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".