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
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.Exists also in English.The other notes of this series are: Faire le lien entre le suivi-évaluation et l'évaluation d'impact ; Introduction aux méthodes mixtes dans l'évaluation d'impact and Utilisation des résultats d'évaluations d'impact. (Available in the Library of Source OSBL and Imagine Canada)
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 | 0.001 | 0.000 |
| 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) | 0.876 | 0.004 |
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