Ethics in the Science Lifecycle: Broadening the Scope of Ethical Analysis
Bibliographic record
Abstract
This chapter offers a simple framework to help structure thinking about ethics considerations in the lifecycle of scientific research. Drawing on the work of an earlier research, the authors have adapted the knowledge-to-action (KTA) cycle in two ways: (i) parsing the key phases in the knowledge creation component of the cycle to match the detail of knowledge application component and highlight the iterative interaction between the two; and (ii) critically considering some of the ethical issues that arise at each phase. The authors propose a pragmatic approach to ethics in the science lifecycle that positions ethics as a critical analysis of relations of power and context. The KTA ethics (KTA-E) framework builds on the KTA cycle and explores some of the ethics considerations at each phase in the science lifecycle from the perspective of relations of power. The chapter discusses sample case scenarios on the application of the KTA-E framework.
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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 | 0.069 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| 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".