The Politics of Youth Participation in Social Intervention Programmes in Ghana: Implications for Participatory Monitoring and Evaluation (PM&E)
Bibliographic record
Abstract
Participatory monitoring and evaluation (PM&E) in project evaluation has gained impetus in recent literature. \nThis paper interrogates youth participation in intervention programmes in Ghana with special reference to Local \nEnterprise and Skills Development Programme (LESDEP). With the aid of primary and secondary data, this paper \nunpacks the questions around programme target beneficiaries, their mode of participation and the impacts of current \nmodels on PM&E. The study reveals the key constraints of youth participation in PM&E, the evolving disapproval of the \ntop-down approach while probing into the existing opportunities. The case study reveals that youth intervention \nprogrammes in Ghana are not only confronted with uncoordinated and overlapping ministries, department and agencies, \nbut also there are power dynamics between stakeholders, in particular, target beneficiaries and programme \nimplementers. The elusive intersection between beneficiaries and the implementing agency impacted negatively on the \nprogramme sustainability. The poor PM&E in youth intervention programmes in Ghana is a key reason that has \nhampered mainstream socio-economic development. The key lesson to be drawn from the case study is the need for \nmatching perspectives of PM&E as well as a recognition and management of power disparities between target \nbeneficiaries and programme implementers. Thus, realizing desired programme objectives will require a different \napproach to structuring, implementing and monitoring of youth intervention initiatives in Ghana.
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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 | 0.086 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".