Translating Project Achievements into Strategic Plans: A Case Study in Utilization-Focused Evaluation
Bibliographic record
Abstract
Background: Utilization-focused evaluation (UFE) is a decision-making framework intended to design and implement evaluations that get utilized. In this case study, the UFE approach was applied to the evaluation of a youth training and employment program in Kenya. Purpose: Analyze a case study based on empirical experience with the lens of evaluation use and influence. Setting: Nairobi and other urban and peri-urban settings, Kenya. Intervention: The evaluation of a youth training and employment program that provided direct training for marginalized youth as well as capacity building for employment and adapted a Basic Employability Skills Training (BEST) model from India. Research design: Analysis of a case study to describe: how the evaluation approach provided enabling factors for funders and grantees to turn evaluation into a learning intervention; the benefit of clarifying a project’s theory of change; and learning how to combine summative and developmental evaluation. Data collection and analysis: A case study based on an evaluation consultancy. The evaluation included site visits, extensive documentation review, qualitative and quantitative data collection. Findings: The ‘facilitation of use’ (a step in UFE) provided a bridge between a summative and a developmental evaluation. The evaluators and program partners developed a learning relationship wherein the evidence influenced the subsequent project design and strategy.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".