Outcomes and Impacts of Development Interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The terms “outcome” and “impact” are ubiquitous in evaluation discourse. However, there are many competing definitions that lack clarity and consistency and sometimes represent fundamentally different meanings. This leads to profound confusion, undermines efforts to improve learning and accountability, and represents a challenge for the evaluation profession. This article investigates how the terms are defined and understood by different institutions and communities. It systematically investigates representative sets of definitions, analyzing them to identify 16 distinct defining elements. This framework is then used to compare definitions and assess their usefulness and limitations. Based on this assessment, the article proposes a remedy in three parts: applying good definition practice in future definition updates, differentiating causal perspectives and using appropriate causal language, and employing meaningful qualifiers when using the terms outcome and impact. The article draws on definitions used in international development, but its findings also apply to domestic public sector policies and interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it