Developing a living lab in ethics: Initial issues and observations
Bibliographic record
Abstract
Living labs are interdisciplinary and participatory initiatives aimed at bringing research closer to practice by involving stakeholders in all stages of research. Living labs align with the principles of participatory research methods as well as recent insights about how participatory ways of generating knowledge help to change practices in concrete settings with respect to specific problems. The participatory, open, and discussion-oriented nature of living labs could be ideally suited to accompany ethical reflection and changes ensuing from reflection. To our knowledge, living labs have not been explicitly trialed and reported in ethics literature. In this discussion paper, we report and discuss four initial issues that marked the process of setting up a living lab in ethics: (1) determining the goals and expected outcomes of an ethics living lab; (2) establishing operational procedures; (3) selecting communities and defining pilot projects; and (4) adopting a lens to tackle emerging questions and challenges. We explain these four issues and present the paths taken based on the novel and specific orientation, that is, living ethics, at the basis of this project. In alignment with living ethics and É-LABO, we approach challenges as learning opportunities to ask not only "how" questions but also "why" questions. We hope that this discussion paper informed by our experience helps to clarify the theoretical, methodological, and practical approaches necessary to successfully adopt and employ living labs in ethics.
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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 | 0.040 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".