Competitive champions versus cooperative advocates
Bibliographic record
Abstract
Background: Evaluation offers non-profit organizations an opportunity to improve their services, demonstrate achievements, and be accountable. The extant literature identifies individuals who can enhance the uptake of evaluation as evaluation champions. However, a paucity of detail is available regarding how to identify them and the support they require. Purpose: This research investigated the characteristics and motivations of evaluation champions and examined how they promoted and embedded evaluation in an organizational system. Setting: Australian human and social service non-profit organizations. Research design: Drawing upon the literature and social interdependence theory, the research took an interpretivist perspective to collaboratively generate knowledge about evaluation champions. The aim was to understand and develop a reconstruction of the characteristics of individuals. This article constitutes a component of a larger research project. Data Collection and Analysis: This research used purposive sampling to recruit champions working in Australian non-profit organizations, who were identified via descriptive criteria gleaned from a literature review. The research involved interviewing 17 champions, four of whom also participated in organizational case studies. Analysis of the semi-structured interviews and case studies generated information about the activities, strategies, motivations, and attributes of individuals who championed and advocated for evaluation. Findings: This article argues that evaluation advocates is a preferable descriptor when attempting to embed evaluation by cultivating mutually beneficial interactions and cooperative working relationships. This research defines evaluation advocates as individuals who motivate others and provide energy, interest, and enthusiasm by connecting evaluation with colleagues’ personal aspirations and the organizational goals to make judgements about effectiveness. This article includes a field guide to facilitate evaluation advocates’ identification, recruitment, support, and development.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".