Producing ‘Human Elements Based Medical Technologies’ in Biotech Companies: Some Ethical and Organisational Ingredients for Innovative Cooking
Bibliographic record
Abstract
This article is based on the findings of an EU-funded qualitative research project, entitled ‘From GMP to GBP: Fostering good bioethics practices [GBP] among the European biotechnology industry’, which seeks to improve the understanding of bioethical issues through the observation of the daily practices in European biotechnology companies and proposes a methodology approaching ethical issues. The comparative study was carried out in biotech companies in France, Italy, Sweden, Hungary and Belgium which develop a wide range of new technologies, all of them involving human materials or where human subjects participate (in clinical trials). Based on our findings in these local settings, we suggest that the notion of bioethics and the way its production is theorised need to be re-conceptualised. We argue that material practices and moral statements are intermingled in inextricable ways that render the formation of bioethical concerns fully dependent on the organisational landscape in which it is embedded. More precisely, the here presented co-production model of moral statements and organisational practices presents a set of common factors that influence how bioethical discourses are shaped, despite the heterogeneity of their epistemic cultures. For example, the procedural design of cell-based-products, the modes of collecting and storing biological specimen, the relationship between patients and companies and technological transfers to emerging countries are defining components that contribute to the shaping process of bioethical concerns. Thus, the path dependency of bioethical concerns relies on an already existing, specific infrastructure and existing relationships within and outside a company rather than on external judgement subsequently applied to its objects, or a collection of processes of reasoning coming from external institutions.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.096 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".