Ethical Reflection Must Always be Measured
Bibliographic record
Abstract
The article analyses what we term governmental ethics regimes as forms of scientific governance. Drawing from empirical research on governmental ethics regimes in Germany, Franceand the UK since the early 1980s, it argues that these governmental ethics regimes grew out of the technical model of scientific governance, but have departed from it in crucial ways. It asks whether ethics regimes can be understood as new ‘‘technologies of humility’’ (Jasanoff) and answers the question with a ‘‘yes, but’’. Yes, governmental ethics regimes have incorporated features that go beyond technologies of prediction and control, but the overcoming of the technical model also bears some ambivalence that needs to be understood. The article argues that governmental ethics regimes can be understood as a form of ‘‘reflexive government’’ (Dean) in that the commitment to techno-scientific innovation is stabilized not through an elitist, technocratic exclusion of non-scientific actors and knowledges or a depreciation of normative and emotional dimensions, but through their inclusion, involvement and mobilization.
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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 | 0.081 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".