The Case for Participatory Evaluation in an Era of Accountability
Bibliographic record
Abstract
Evaluation occurs within a specific context and is influenced by the economic, political, historical, and social forces that shape that context. The culture of evaluation is thus very much embedded in the culture of accountability that currently prevails in public sector institutions, policies, and program. As such, our understanding of the reception and use of participatory approaches to evaluation must include an understanding of the practices of new public management and of the concomitant call for accountability and performance measurement standards that currently prevail. In this article, the author discusses how accountability has been defined and understood in the context of government policies, programs, and services and provides a brief discussion of participatory and collaborative approaches to evaluation and the interrelationship between participatory evaluation and technical approaches to evaluation. The main part of the article is a critical look at key tensions between participatory and technocratic approaches to evaluation. The article concludes with a focus on the epistemological and cultural implications of the current culture of public accountability.
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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 | 0.384 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.148 |
| Scholarly communication | 0.037 | 0.058 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.021 | 0.031 |
| 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; 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".