An assessment of engaged social science research in nanoscale science and engineering communities
Bibliographic record
Abstract
Abstract Increased funding of nanotechnology research in the USA at the turn of the millennium was paired with a legislative commitment to and a novel societal research policy for the responsible development of nanotechnology. Innovative policy discourses at the time suggested that such work could engage a variety of publics, stakeholders, and researchers to enhance the capacity of research systems to adapt and be responsive to societal values and concerns. This article reviews one of two federally funded social science research centers—the Center for Nanotechnology in Society at Arizona State University(CNS-ASU)—to assess the merits of this form of engaged social science research in which social science contributes not only to traditional knowledge production but also to the capacity of natural science and engineering researchers and research communities for greater reflexivity and responsiveness, ultimately producing more socially robust research systems.
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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 | 0.156 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".