Ethical Regulations of Medical Research Involving Human Subjects: Exploring the Perspective of Trial Participants
Bibliographic record
Abstract
In this paper I address the question of whether the existing ethical regulations of clinical research ensure protection and well-being of human subjects. Drawing on ethnographic data gathered in Berlin, Germany, I show that German institutions which are meant to ensure the ethical validity of clinical research cannot address posed issues. It appears that these institutions (Berlin Ethik-Kommission in particular) only evaluate research protocols and do not consider the broad spectrum of processes and interactions involved in clinical research. The experience of professional human subjects, as well as the consideration of the every-day life in a clinic, shows that there is much more to clinical trials. The argument of this paper is that the inability of institutions to address protection of human subjects originates from the bureaucratic logic of their organization. Drawing on Bauman’s (1992) argument that the bureaucratic machine is characterized by separation between morality and purpose, with the example of Berlin Ethik-Kommission, I argue that the bureaucratic machine cannot be sensitive to morality and ethics, even if these are its main purposes.
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.
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 | 0.519 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.095 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.021 | 0.027 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".