Evaluation Literacy: Perspectives of Internal Evaluators in Non-Government Organizations
Bibliographic record
Abstract
Abstract: While there is an abundance of literature on evaluation use, there has been little discussion regarding internal evaluators’ role in promoting evaluation use. Evaluation can be undervalued if context is not taken into consideration. Evaluation literacy is needed to make evaluation more appropriate, understandable, and accessible, particularly in non-government organizations (NGOs) where there is a growing focus on demonstrable outcomes. Evaluation literacy refers to an individual’s understanding and knowledge of evaluation and is an essential component of embedding evaluation into organizational culture. In recognition of the value of the internal perspective, a small exploratory exercise was undertaken to reveal internal evaluator roles and ways of engaging with colleagues around evaluation. Th e exercise examined a key question: What is the role of evaluation literacy in internal evaluation in the non-government sector? Three Australian auto-narrative examples from internal evaluators highlight evaluation literacy and locate it among the multiplicity of roles required for optimal evaluation uptake. Analysis of the narratives revealed the underlying issues affecting evaluation use in NGOs and the skills needed to motivate and enable others to access, understand, and use evaluation information. Responding to the call for expanded research into internal evaluation from a practice perspective, the authors hope that the findings will stimulate a wider conversation and further advance understanding of evaluation literacy.
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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 | 0.086 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".