Conflicts of Interest in the Assessment of Chemicals, Waste, and Pollution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pollution by chemicals and waste impacts human and ecosystem health on regional, national, and global scales, resulting, together with climate change and biodiversity loss, in a triple planetary crisis. Consequently, in 2022, countries agreed to establish an intergovernmental science-policy panel (SPP) on chemicals, waste, and pollution prevention, complementary to the existing intergovernmental science-policy bodies on climate change and biodiversity. To ensure the SPP's success, it is imperative to protect it from conflicts of interest (COI). Here, we (i) define and review the implications of COI, and its relevance for the management of chemicals, waste, and pollution; (ii) summarize established tactics to manufacture doubt in favor of vested interests, i.e., to counter scientific evidence and/or to promote misleading narratives favorable to financial interests; and (iii) illustrate these with selected examples. This analysis leads to a review of arguments for and against chemical industry representation in the SPP's work. We further (iv) rebut an assertion voiced by some that the chemical industry should be directly involved in the panel's work because it possesses data on chemicals essential for the panel's activities. Finally, (v) we present steps that should be taken to prevent the detrimental impacts of COI in the work of the SPP. In particular, we propose to include an independent auditor's role in the SPP to ensure that participation and processes follow clear COI rules. Among others, the auditor should evaluate the content of the assessments produced to ensure unbiased representation of information that underpins the SPP's activities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it