Public Policy and Youth Employment: An Empirical Study of Cameroon's Experience
Bibliographic record
Abstract
The aim purpose of this paper is to assess the contribution of public policies on youth employment in Cameroon. To do this, we used the multinomial Logit model that is being followed up for our employment equation. The maximum probability method is the estimation technique used and applied to data extracted from the EISS database (2011). Three main results emerge from this study: (1) young people who wish to self-employment do not have adequate training and the technical and financial support offered to them by the government is insufficient; (2) the incentives proposed by the State to private operator to encourage them to recruit young people do not always contribute to this objective and (3) the massive recruitments carried out by the State fail to pay off all unemployed young people. In this situation, the Cameroonian state should further strengthen the professionalization of training and, above all, guide training offers in the areas that present opportunities in our country. It also needs to strengthen the facilities afforded to private companies to encourage them to recruit more young people. We also suggest that the Cameroonian government provide more technical and material support to young people who are seeking it and, on the other hand, to raise more funds for the bankable projects presented by these Last.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".