Openness and the governance of human stem cell lines: a conceptual approach
Bibliographic record
Abstract
My research examines the extent to which features of ‘openness’ might usefully contribute to mechanisms of governance of human stem cell lines, with a view to the production of therapeutic stem cell treatments for the provision of health benefits. The impetus for the project is the UK Stem Cell Bank, a national repository for stem cell lines and the focal point of a unique set of publicly supported, non-statutory arrangements for the informal (but mandatory) oversight of human embryonic stem cell lines (hESCs) in the UK. The sharing of stem cells through this mechanism promotes public confidence in embryo and stem cell research, and supports research by making (ethically-sourced and quality-controlled) human stem cell lines widely available to researchers, but the structure and functions of the Bank also impose constraints on the imminent commercial development and manufacture of stem cell therapies for human application. My thesis examines the role of ‘openness’ in systems of governance designed to facilitate not just research but the whole trajectory of stem cell technology, from research to production and delivery of clinical treatments. What is openness and what function does it have in purposive attempts to design mechanisms that will advance stem cell technology? The bulk of my thesis maps out the conceptual foundations upon which systems of governance for the production of stem cell therapies may be grounded. It does not address the ethical and social debate surrounding embryo research and the embryonic derivation of stem cell lines, which are legally permissible in the UK. In Part I, I frame the problem of governance of ongoing use of stem cell lines as part of a larger policy endeavour related to the provision of public goods. Secondly, I propose a conception of reflexive governance that is capable of facilitation of technology in a multi-faceted heterogeneous environment. Part II explores traditional narratives of openness in science and technology, and how they might be reconceived in the context of modern scientific technology. In Part III, I apply my conception of facilitative governance to collective strategies or ‘commons’ approaches to facilitative governance. I then identify its applicability for the present UK system governing stem cell lines, and for the proposition of alternative structures and processes that might be better able to achieve the policy goal of provision of health benefits through delivery of therapeutic stem cell treatments.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.079 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".