Bibliographic record
Abstract
The general public often fears the wrong risks and has a blind spot when it comes to global existential risks. People have divergent and emotive attitudes to risk rather than rational understanding. For this reason, public understanding of risk and risk governance promotes a non-tendentious and theory-neutral approach designed in a way so that laypersons can become aware of, make sound judgments about and take action in terms of risk. Public understanding refers to the creation of scientific literacy, sagacity, and decisional competence within a broad public. The public understanding encompasses two primary dimensions: knowledge and rationality. They empower the public’s ability to adopt an impartial perspective which is essential to ensure the democratic formation of public opinion and political will. I argue that the public understanding can be established through the engagement of the public. I explore and conceptualize the generation and propagation of the public understanding of risk and risk governance through three elements of understanding: epistemic, ontological, and teleological. These elements constituting public understanding are produced by a tripartite functional differentiation of deliberative production, namely an interplay between scientific, associational and public deliberation within risk governance.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".