Exploring the Issues: An Evaluation Literature Review
Bibliographic record
Abstract
Finding ways to make evaluation more meaningful and more useful has been a key theme in the evaluation literature since the discipline began, and there is no shortage of discussion around improving evaluation among nonprofit practitioners. The topic has been a highlight at ONN's annual conference in recent years.However, much of the discussion around improving evaluation focuses on methodology, tools, and indicators.There has been less attention paid to who is asking and determining the questions of evaluation, such as who evaluation is for and what is its purpose. Consequently, the purpose of this background paper is to review the literature on evaluation use with a particular focus on systemic factors. In other words, we are interested in looking at the relationship between evaluation practice and the overall structure and function of the nonprofit sector in Ontario.We're interested in the policies and regulations that guide us, the roles played by various actors, theassumptions we make, the language we use, and the ways in which resources move through the sector. We're examining the purposes that evaluation serves, both overt and implicit. We want to learn more about the factors that make evaluations really useful, the issues that can get in the way of evaluations being useful, and ideas for improvement. Ultimately, our goal in this paper is to generate a broad vision to inform our project's final outcomes.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".