Applications of contribution analysis to outcome planning and impact evaluation
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
Contribution analysis is a structured approach to theory-based impact evaluation originally developed in Canada in the context of Results-Based Management (RBM) although there have been few examples of contribution analysis in practice since Mayne’s original paper (2001). We argue that contribution analysis adds value to other theory-based evaluation approaches by providing a more structured and rigorous approach to participatory evaluation planning, data analysis and reporting. It can be applied in the context of participatory strategic planning and performance monitoring as well as impact evaluation. Examples are drawn from Scotland and Canada in the performance context of RBM in Canada and Outcomes-Based Accountability (OBA) in Scotland. The authors argue that, as a participatory process, contribution analysis strengthens both conceptual and practical understanding of planning/managing for outcomes and implementation and change theories, thus helping to build collaborative capacity within and across partner organizations. For public managers, the contribution analysis process has a strong appeal and practical value when faced with the task of demonstrating the contribution of single organizations to addressing complex social issues while working in partnership with other public agencies facing multiple accountabilities.
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 | 0.035 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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