How to normalize reflexive evaluation? Navigating between legitimacy and integrity
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
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.
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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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