Debate on the Appropriate Methods for Conducting Impact Evaluation of Programs within the Development Context
Bibliographic record
Abstract
Background: Donors and decision-makers use impact evaluation reports to assess the effectiveness of development programs and identify ways to improve the design and implementation of projects, programs, and policies in developing countries. Purpose: This paper will explore the existing published impact evaluation literature on development programs and provide an overview of the types of approaches and methods that are being used to conduct impact evaluations. Setting: NA Intervention: NA Research Design: The paper will examine published program evaluation literature in order to shed light on issues related to appropriate methods for impact evaluations of development programs. Data Collection and Analysis: Literature review. Findings: The paper will conclude by suggesting a list of approaches and methods that can be used to conduct impact evaluations of programs within the development context.
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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 | 0.521 | 0.590 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.009 | 0.015 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".