Introduction to Impact Evaluation
Bibliographic record
Abstract
This is the first guidance note in a four-part series of notes related to impact evaluation developed by InterAction with financial support from the Rockefeller Foundation.This first guidance note, Introduction to Impact Evaluation, provides an overview of impact evaluation, explaining how impact evaluation differs from -- and complements -- other types of evaluation, why impact evaluation should be done, when and by whom. It describes different methods, approaches and designs that can be used for the different aspects of impact evaluation: clarifying values for the evaluation, developing a theory of how the intervention is understood to work, measuring or describing impacts and other important variables, explaining why impacts have occurred, synthesizing results, and reporting and supporting use. The note discusses what is considered good impact evaluation -- evaluation that achieves a balance between the competing imperatives of being useful, rigorous, ethical and practical -- and how to achieve this.The other notes in this series are: Linking Monitoring & Evaluation to Impact Evaluation (http://sectorsource.ca/node/8261); Introduction to Mixed Methods in Impact Evaluation (http://sectorsource.ca/node/8254); and Use of Impact Evaluation Results (http://sectorsource.ca/node/8263). (Available in the Library of Source OSBL and Imagine Canada)Also available in French.
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.122 | 0.052 |
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".