Experts’ understanding of the public: knowledge control in a risk controversy
Bibliographic record
Abstract
This paper reverses the common emphasis in the literature on public understanding of science by examining “experts’ understanding of the public.” This research uses the case of aquaculture in Canada, a highly contested mode of production that has divided the scientific community and public opinion. Using a survey of 300 aquaculture experts in Canada, we examine three dimensions of experts’ understanding of public “contributions” to this controversy. These are (1) stakeholder participation in aquaculture regulation and policy, (2) the media as an interpreter and communicator of expert claims, and (3) the knowledge and values basis of general public opinion. We find that experts’ views on lay knowledge and participation in the debate swing from strongly positive to strongly negative. Using quantitative and qualitative data from the survey, we argue that these swings in experts’ views of the public coincide with issues of control over knowledge. Experts on all sides of the aquaculture controversy are markedly open to incorporating lay knowledge into scientific practices (a situation where expert control over knowledge is retained), but are highly critical of lay “consumption” of expert claims (a situation where expert control over knowledge is lost).
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 | 0.048 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.019 | 0.055 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".