Informing Evaluation Capacity Building Through Profiling Organizational Capacity for Evaluation: An Empirical Examination of four Canadian Federal Government Organizations
Bibliographic record
Abstract
Abstract: According to the literature published on the topic, the development of an organization’s capacity to do and use evaluation typically follows four stages: traditional evaluation, characterized by externally mandated evaluation activities; awareness and experimentation, during which organizational members learn about evaluation and its benefits by participating in a number of evaluation-related activities; evaluation implementation, the stage at which the role of evaluation is more clearly defined in the organization; and evaluation adoption, which occurs when evaluative inquiry becomes a regular and ongoing activity within the organization through the allocation of continued financial and human resources. In this article we argue that this perspective is oversimplified and that it is essential to understand the complexity of an organization’s evaluation capacity in order to better understand how it might proceed with evaluation capacity building (ECB). We present an analysis of four Canadian federal government organizations’ self-assessment of their organizational evaluation capacity using a profile conceptual framework developed as part of our larger study. We then integrate the resulting multidimensional profiles of observed levels of organizational evaluation capacity with the aforementioned stages of ECB to provide added value in thinking about the direction of organizational ECB.
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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".