Differentiated Analysis of the Occupational Integration of Young Agricultural Bachelor Degree Holders in Agricultural Entrepreneurship: Case Study from the University of Abomey-Calavi in the Republic of Benin
Bibliographic record
Abstract
At a time when agricultural entrepreneurship is increasingly recognized as a crucial alternative to unemployment and underemployment, particularly in developing countries, it is becoming imperative to have baseline or reference information on entry into the agricultural entrepreneurship sector. This article gives an overview of the integration of 1,305 agricultural bachelor degree holders into agricultural entrepreneurship in Benin. These bachelors studied and graduated in the bachelor-master-doctorate program (labeled LMD program) of the Faculty of Agricultural Sciences of the University of Abomey-Calavi (FSA/UAC) between 2011 and 2020. Data related to the occupational integration of this target population were collected with the help of online surveys. These data were statistically analyzed using Excel v.2019 and STATA v.15. Results reveal an overall integration rate of 16.17%, with significant variations ranging from 6% to 27% from one year to the next. Gender disparities are also notable, with only 29.52% of target bachelors entering agricultural entrepreneurship as women.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".