The metis of responsible innovation : helping society to get better at the conversation between today and tomorrow
Bibliographic record
Abstract
The metis of responsible innovationHelping society to get better at the conversation between today and tomorrow Esteemed Rector Magnificus, colleagues, family and friends For most of my academic career I have been driven by the simple idea that the future could be otherwise (Irwin 2016).This is a shorthand for a complex set of arguments that seek to inject social agency into technological decision-making, the argument being that unless we find ways to shape science and innovation in tune with widely shared social values, future changes will occur without explicit societal shaping, commonly driven by the power of incumbent interests and the delegation of 'the good' to market forces.Thus, over the years, I have become engaged with a variety of technological domain areas -focusing predominantly on what my friend Richard Owen calls 'techno-visionary' science and innovation -exploring ways to understand the social and ethical issues associated with emerging science and technology at an upstream stage, and to develop novel frameworks of governance that can accommodate them.In pursuit of the latter I have become implicated in the development of frameworks of responsible innovation.In this inaugural lecture, using the ancient Greek idea of metis, defined as the practical skills and acquired knowledge required to respond to a constantly changing environment, I speak to the craft of doing responsible innovation, how it was put to use in devising a framework for the UK research councils and how in the context of particular technological innovations -using nanotechnology, climate geoengineering and agricultural biotechnology as cases -academic and policy understandings have to some extent been reconfigured and new directions opened up.I conclude with reflections on Wageningen as a site for collaboration on the crafting of responsible futures.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".