A web-based Instrument to Model Social Norms: NERD Design and Results
Bibliographic record
Abstract
Surveys and focus groups are well known methods for ascertaining public perceptions and opinion. The general view is that such tools provide reasonably accurate reflections of public values, and that the norms employed by people to make decisions are fixed. But what about issues where the public needs to consider novel choices where no prior experience can be drawn on? Do their preferences and beliefs change when presented with new options and new information? Recent evidence suggests they do and this paper describes an alternative way of gathering data, which takes into account the dynamic nature of social norms in response to new technologies and their applications. It also discusses the problem with traditional methods of generating information about public opinion and offers a possible solution. Our interdisciplinary research team, NERD (Norms Evolving in Response to Dilemmas), has developed a web-based survey instrument that is designed to bridge the gap between perceived and actual public opinion, which traditional surveys and focus groups are unable to capture. This paper will present some of our preliminary findings from the results of our first survey on the topic of Human Health and Genomics. We have found that there are differences in the way respondents answer which has not yet been accounted for in other participatory processes. If new technologies demand new methods for creating policies, then it is imperative to find solutions that the older, more traditional methods currently face.
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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 | 0.066 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".