“Advocates Change the World; Evaluation Can Help”: A Literature Review and Key Insights from the Practice of Advocacy Evaluation
Bibliographic record
Abstract
Abstract: It is only since the new millennium that assessments of policy and systems change initiatives have been given much attention in practice or in research. A comprehensive literature review of advocacy in human services non-profit organizations (NGOs) has found a “lack of systemic, rational evaluation and measurement of the effectiveness of advocacy.” Similarly, in a survey of 211 NGOs that undertake advocacy, only one in four (24.6 percent) reported that this work had been evaluated. While the survey was conducted in the United States, it is likely that Canadian NGOs are in a similar situation, not because of a lack of interest or value for evaluation but, rather, because assessing systems change initiatives is challenging territory and NGOs face considerable resource and time constraints that put such evaluation low on the priority list. This article provides a review of key insights from advocacy evaluation practice and research that may help orient and inform NGOs as they decide on evaluation strategies. I will outline the state of the field of systems- and policy-change evaluation in North America as well as its benefits and challenges. Finally, a synthesis of the main steps in advocacy evaluation planning and implementation offers a broad map to NGOs seeking to enhance this practice in their own organizations.
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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 | 0.044 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".