Toward an Evidence-Based Approach to Building Evaluation Capacity
Bibliographic record
Abstract
Abstract: Organizations are required to evaluate their programs for both learning and accountability purposes, which has increased the need to build their internal evaluation capacity. A remaining challenge is access to tools that lead to valid evidence supporting internal capacity development. The authors share practical insights from the development and use of the Evaluation Capacity Needs Assessment tool and framework and implications for using its data to make concrete decisions within Canadian contexts. The article refers to validity evidence generated from factor analyses and structural equation modelling and describes how applying the framework can be used to identify individual and organizational evaluation capacity strengths and gaps, concluding with practice considerations and future directions for this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.454 | 0.508 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.041 | 0.019 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.041 | 0.034 |
| Open science | 0.012 | 0.035 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".