Insights into Evaluation Capacity Building: Motivations, Strategies, Outcomes, and Lessons Learned
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: Evaluation capacity building (ECB) is a topic of great interest to many organizations as they face increasing demands for accountability and evidence-based practices. While many evaluators are engaged in evaluation capacity building activities and processes with a wide variety of organizations, we still know very little about whose capacity is being built, what strategies are being used, and the overall effectiveness of these efforts. To explore these issues, a research study was conducted with 15 organizations that have been involved in ECB efforts during the last few years. The findings reported in this article are part of a larger study, and represent interviews with 25 evaluators and 13 clients (n = 38), who have facilitated and supported an organization’s ECB effort. We specifically focus on the participants’ motivations for engaging in ECB, the teaching and learning strategies used to facilitate capacity building, their perceived outcomes of this effort, and their lessons learned.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it