Critical Connections between Participatory Evaluation, Organizational Learning and Intentional Change in Pluralistic Organizations
Bibliographic record
Abstract
The current debate around the emergence of participatory approaches in evaluation practice suggests that participatory evaluation may be considered an organizational learning praxis, one which facilitates the development of a holistic process of intentional change. Through critical reflection on how participatory evaluation has been conceptualized, this article offers an overview of some of the contextual challenges encountered when using participatory evaluation to enable the creation of learning environments. Given the pluralistic nature of modern organizations and some contextual constraints, evaluators appear to have largely developed a more instrumental type of learning, which may, paradoxically, result in a significant source of resistance to intentional change. This article proposes a process of capacity building for evaluative research (CBER). This process offers a collaborative way of overcoming unforeseen resistance to intentional change by overcoming the challenges found in the relationship between participatory evaluation and organizational learning. The article concludes by suggesting some epistemological and organizational issues that evaluators should take into account when enabling the implementation of a process of CBER in pluralistic organizations.
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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 | 0.178 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.118 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".