Impact of mentoring on the likelihood of getting jobs in the agricultural sector in Benin
Bibliographic record
Abstract
This study evaluates the impact of mentoring programs on the likelihood of getting a job in the agricultural sector after a one-year experiment conducted in Benin. The program provides graduates in agriculture-related fields with capacity building (digital skills, job search skills, and interpersonal skills) – as well as the support of a professional who is either a junior (junior model) or a senior (senior model) – as they seek jobs. The evaluation framework followed a mixed-methods design that incorporated survey data and qualitative data. The findings from the randomised controlled trial (RCT) showed a positive impact of the senior mentoring model, which increased the likelihood of getting a job in the agricultural sector by 16.4 per cent. In addition, the senior mentoring model had more impact on the likelihood of getting a job for both genders with an increase of 18.7 per cent for men and 11.9 per cent for women. Furthermore, mentees valued receiving practical career-related assistance, a realistic perspective on the workplace, and psychological and emotional support. The study suggests the need for a comprehensive policy package by policymakers and the institutionalisation of a formal mentoring program by youth-serving organisations based on the senior model.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".