Forever chemicals: Heterogeneity in expert's beliefs about PFAS and what to do about it
Bibliographic record
Abstract
The associated health risks of persistent, mobile and toxic chemicals - found to accumulate in both humans and the environment - are receiving growing media attention, prompted by high profile films and documentaries such as The Devil We Know (2018) and Dark Waters (2019). As such, the public are becoming increasingly aware of the risks of exposure to chemicals like per- and poly-fluoroalkyls substances (PFAS), and regulators are turning to the experts for advice on where PFAS is found, and how to avoid it. But how much do the experts agree with one another on this rapidly emerging global pollutant crisis? We collected data from N = 40 experts (i.e., chemists and epidemiologists specializing in such substances) regarding: a) the sorts of everyday products that they believe PFAS are found in, b) how essential these everyday products are deemed to be for personal and societal functioning, and c) how easily PFAS can be substituted in these products for PFAS-free alternatives. While analysis is ongoing at the time of abstract submission (Feb, 2022), our findings already demonstrate a surprising level of heterogeneity for all outcomes across our expert sample. A lay sample is currently being collected so that perceptions of PFAS prevalence and substitutability, and product essentiality can be compared across experts and non-experts. Such comparisons will allow us to identify critical knowledge gaps in the public’s understanding of PFAS pollution and risks. Building on insights from the climate change literature, we will discuss implications for designing clear messaging and advice for lay and policy audiences when heterogeneity among the experts is prevalent. In particular, we use our findings to discuss obstacles and solutions for overcoming expert heterogeneity in specific cases, for example, when essentiality is generally perceived to be high but substitutability is low (e.g., PFAS in facemasks recommended by policy-makers to reduce the spread of SARS-CoV-2), and ask what effective, tailored communication might look like across different demographics.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".