Bibliographic record
Abstract
Generally, public opinion is measured via polls or survey instruments, with a majority of responses in a particular direction taken to indicate the presence of a given ‘public opinion’. However, discursive psychological and related scholarship has shown that the ontological status of both individual opinion and public opinion is highly suspect. In the first part of this article I draw on this body of work to demonstrate that there is currently no meaningful theoretical foundation for the construct of public opinion as it is typically measured in surveys, polls, or focus groups. I then argue that there is a particular sense in which the construct of public opinion does make sense. In deliberative democratic forums participants engage in dialogue with the aim of coming to collective positions on particular issues. Here I draw on examples of deliberative democratic forums conducted on the social and ethical implications of science and technology. Conversation between participants in deliberative democratic forums is ideally characterized by individuals becoming informed about the issues being discussed, respectful interactions between participants, individuals being open to changing their positions, and a convergence towards collective positions in the interest of formulating civic solutions. The end-product of deliberation on a given issue might thus be termed a deliberative public opinion. ‘Deliberative public opinion’ is neither a cognitive nor an aggregate construct, but rather a socio-historical product. Criteria for its legitimacy rely on the inclusiveness of diversity of perspectives and the degree to which collective positions are defensible to a larger society.
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 | 0.066 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".