Social Innovation through Evaluation Science Dynamic Learning Approaches for Nonprofit Leaders Driving Social Change
Bibliographic record
Abstract
Program evaluation, as a learning process, offers promise for nonprofit leaders. Rather than conducting research on organizations, evaluators partner with organizational leaders, helping them, in real-time, understand their programs, policies, and systems; prioritize their informational needs; and generate credible and actionable knowledge to answer their questions. This article 1) outlines why evaluation may be a more responsive learning process for nonprofit leaders, in contrast to other research processes; 2) describes the field of program evaluation and its contributions to knowledge creation for nonprofits; 3) explains an evaluation approach, called developmental evaluation, that produces real-time, complexity-informed knowledge resources for nonprofit leaders; and 4) concludes by encouraging nonprofit leaders to make the most of processes, like developmental evaluation, by building their capacity to promote sustained organizational learning and knowledge creation that is conducted for them and by them as opposed to on them.
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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 | 0.054 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".