Contributions of Ethnography of Science to Judicial Assessment of Environmental Expert Testimony
Bibliographic record
Abstract
Judges are challenged by complex scientific expert testimony in cases involving environmental impacts of modern industrial processes. 1 While there is a long history of forensic, psychological and genetic evidence presented in court, 2 various ‘proof and truth’ challenges are heightened by growing awareness of environmental threats and scientific conflicts over risk. We illustrate these challenges by discussing two Canadian court cases involving the impact of aquaculture pesticide use on a commercial lobster fishery. We then turn to our four-year research project on environmental risk in the marine environment to explore the contribution ethnographic studies of environmental science can make towards a legal understanding of the associated scientific challenges. The research examined how scientists, resource users, and participants in marine industries perceived risk to the ocean environment differently, how science was undertaken to evaluate such risks and under what political influences, and how the incorporation of local knowledge contributed to scientific research outcomes. Such ethnographic analysis can shed light on scientific practice and in turn suggest the limitations of some current approaches to judicial evaluations of scientific evidence, particularly the guidelines arrived at in Daubert , which utilize an arguably unrealistic view of science. 3 We highlight several problems with judicial evaluation of science in environmental cases: the unrealistic expectations of the science of risk; difficulties encountered in field studies of environmental impacts versus lab studies; and the potential for deconstruction of science in court as such field studies increasingly employ Post-Normal Science (PNS) methods. 4 We conclude that ethnographic analysis of the co-construction of knowledge, common in PNS, would contribute to a more nuanced approach to the judicial assessment of the science of environmental risk analysis. Our methods used to further the co-construction of knowledge may also prove helpful to lawyers working with stakeholders in cases involving environmental risk.
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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 | 0.065 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".