Using contribution analysis to assess evaluation capacity outcomes in community organizations
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
Abstract Community organizations face challenges in addressing the needs of their members and beneficiaries, particularly during times of crisis. More than ever, evidence‐informed programming and decision‐making are essential to help organizations “do more with less”; however, community organizations seldom have the individual and organizational evaluation capacity required to conduct and use evaluations to inform decision‐making. This article provides an overview of the evaluation of the LaboEval evaluation capacity building (ECB) intervention, a 5‐year program implemented in 16 organizations. LaboEval brings together direct and indirect individual and organizational ECB activities to enhance evaluation capacity. Contribution analysis (CA) was used as the evaluation approach in this study, assessing the intervention's activities, outputs, outcomes, and assumptions. Data sources include interviews, document reviews, surveys, administrative data, and a literature review. This evaluation contributes to the field by applying CA to a multiyear, multi‐organizational ECB intervention. The findings inform evaluation capacity builders and evaluators interested in CA, showcasing a unique approach to evaluating complex interventions across multiple organizations.
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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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