Analytical Paradigms: The Epistemological Distances between Scientists, Policy Makers, and the Public
Bibliographic record
Abstract
The effective use of evidence and its resultant knowledge is increasingly recognized as critical in risk analysis. This, in turn, has led to a growing concern over issues of epistemology in risk communication, and, in particular, interest in how knowledge is constructed and employed by the key players in risk--scientists, policy makers, and the public. This article uses a critical theoretical approach to explore how evidence is recognized and validated, and how limits are placed on knowledge by scientists, policy makers, and the public. It brings together developments in the sociology of science, policy and policy development, public understandings of science, and risk communication and analysis to explicate the differing forms of rationality employed by each group. The work concludes that each group employs different, although equally legitimate, forms of rationality when evaluating evidence and generating knowledge around risky environment and health issues. Scientists, policy makers, and the public employ scientific, political, and social rationality, respectively. These differing forms of rationality reflect underlying epistemological distances from which can develop considerable misunderstandings and misinterpretations.
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 | 0.115 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.012 | 0.135 |
| Scholarly communication | 0.033 | 0.038 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".