Utilization Focused Developmental Evaluation: Learning Through Practice
Bibliographic record
Abstract
Background: Utilization-focused evaluation provides an overall decision-making framework with the intention of ensuring evaluation products and processes are actually used. Developmental evaluation provides a structure to learn from an experiment or pilot in the making and provide feedback to course-correct and improve the ongoing effort. In this paper we report on a project where we combined both into a utilization-focused developmental evaluation (UFDE). Purpose: To determine the theoretical complementarities and the practical advantages of combining UFE with DE, by reflecting on a practical experience. We include a synopsis of the methodology along with a sample of findings, followed by a reflection of the overall process. We emphasize the conditions that enabled this experience to evolve to guide other practitioners interested in this learning approach to evaluation. Setting: The context was the piloting of a social and financial education curriculum for youth called Aflateen that was developed by Aflatoun Child Savings International in Amsterdam and test-driven by over forty partners around the World. Intervention: The evaluation experience took place during a ten-month period between December 2012 and October 2013. This paper provides a summary of the context and a justification for the approach. Research Design: Not applicable. Data Collection and Analysis: In additional to conventional data collection instruments, we applied participatory inquiry tools from Social Analysis Systems (www.sas2.net) as a means of engaging stakeholders in real-time data collection and analysis. Findings: Examples are provided to demonstrate how some developmental evaluation findings informed organizational strategic and operational decisions during the life of the evaluation.
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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 | 0.203 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".