Impact of mentoring on the likelihood of getting jobs in the agricultural sector in Benin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it