How to normalize reflexive evaluation? Navigating between legitimacy and integrity
Bibliographic record
Abstract
While hybrid evaluation practices are increasingly common, many Western countries continue to favor modernist evaluation logics focused on performance management—hampering the normalization of reflexive logics revolving around system change. We use Normalization Process Theory to analyze the work evaluators from a policy assessment agency undertook to accomplish the alignment between the prevailing and proposed logics guiding evaluation practice, while implementing a reflexive evaluation approach. Ad hoc alignment strategies and insufficient investment in mutual sense-making regarding reflexive evaluation hindered normalization. We conclude that alignment requires developing reflexive evaluation legitimacy in the context of application and guarding reflexive evaluation integrity, while contextual structures and cultures and reflexive evaluation components are being negotiated. Elasticity (of contextual structures and cultures) and plasticity (of reflexive evaluation components) are introduced as helpful concepts to further understand how reflexive evaluation practices can become normalized. We reflect on the use of Normalization Process Theory for studying the normalization of reflexive evaluation.
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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 | 0.182 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.029 | 0.038 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".