Helping young people in South Africa bridge the gap between intention and behaviour in their search for work
Bibliographic record
Abstract
South Africa, like many countries, faces an enormous burden of youth unemployment, with more than one-third of 15 to 34 year-olds without work in the first quarter of 2018 (Statistics South Africa, 2018[1]). Anecdotal evidence suggests that high and persistent unemployment is making South African youth feel increasingly discouraged in their search for work. Indeed, the job seekers in our sample of youth who were motivated enough to come to a job centre spent an average of 11 hours per week searching for employment, but submitted only approximately four applications per month. These youth want to intensify their job search, and they set aside the time and make plans to do so; however, their behaviour does not match their intentions. We set out to understand whether there were simple, low-cost, accessible tools that could help unemployed youth to follow through on their job search goals (Abel et al., 2018[2]).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".