Classifying biotechnology-related policy, regulatory and innovation regimes: A framework for the comparative analysis of genomics policy-making
Bibliographic record
Abstract
Abstract An important part of the study of the policy response of government in the area of a novel technology, such as genomics, lies in identifying the technological trajectory followed in the sector and how it intersects or impacts upon existing policy, regulatory and innovation regimes. Part of the challenge in studying the impacts and outcomes of such trajectories, therefore, is their multilayered nature. This has led to the proliferation of different models or frameworks for the analysis of many sectors, each one tackling a specific level and obscuring the linkages between levels and units of analysis. Research into innovations, however, benefits from an understanding of the overall policy and regulatory regimes present in a sector while an understanding of regulatory behaviour is in turn linked to the overall policy framework set up to govern a sector. As such, analyses of both regulation and innovation in a sector such as genomics can profit from an integrated, multi-level approach grounded in the overall nature of the policy regime present in the sphere of activity under examination. We offer such an approach by synthesizing four existing models of policy, regulatory and innovation behaviour that fit the three levels of analysis – the policy regime, regulatory regime and the innovation regime – in the sphere of biotechnology.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".