Bibliographic record
Abstract
This article presents a reflection on the challenges and opportunities associated with the now ubiquitous requests inviting social scientists to participate in ELSI (Ethical, Legal and Social Implications) type frameworks, attached to large science projects such as nanotechnoscience. It elaborates on some ideas presented in a panel discussion titled "On the Social and Ethical Impacts of Nanotechnology" in Winnipeg at the Canadian Sociological and Anthropological Association (CSAA) annual meeting in the spring of 2004. This was the first panel session devoted to nanotechnology in the CSAA. I begin by briefly developing some key ideas from the field of social studies of science in order to draw attention to the fact that scientific activity has always required the mobilisation of a variety of social, political, cultural and economic resources. Nanotechnoscience is no different. What is distinctive, however, is the perceived need to enrol the social sciences in ELSI-type programs as a way securing legitimacy and to contribute to the overall success of these initiatives. I suggest that it is important to attend to the types of discursive spaces and objects of knowledge that are opened up to the social sciences in these ELSI frameworks. In light of work in science studies, the notion that the social implications of the technology can be grasped by simply projecting current trends into the future has to be problematised and treated with great care. I conclude by suggesting that sociology and anthropology's most important contribution might lie not in contributing to the illusion of predictability and control, which nanotechnoscience is currently attempting to foster as a way of securing social, political, ethical and economic legitimacy for its endeavour, but in short-circuiting these processes.
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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 | 0.042 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.047 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.035 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".