Unveiling Chemical Industry Secrets: Insights Gleaned from Scientific Literatures that Examine Internal Chemical Corporate Documents – A Scoping Review
Bibliographic record
Abstract
We conducted a systematic search using broad and case study-derived keywords, detailed in the appendix. This resulted in 318 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo's ghost management framework. The final results included and analyzed 15 scientific papers (3–17). Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n=13), regulatory capture (n=13), professional capture (n=7), civil society capture (n=6), media capture (n=4), legal capture (n=4), technological capture (n=3), and market capture (n=2). The limited scientific literature meeting our criteria confirms early findings by Wieland et al (18), highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, unveils a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong's pharmaceutical corporate capture review, which identified 37 papers before 2022 (1), our chemical industry findings reveal a lower count, with only 15 papers identified. Comparing pharmaceutical and chemical scoping reviews, lower variations emerge across scientific (n=28 vs. n=13), professional (n=16 vs. n=7), and market captures (n=4 vs. n=2). The chemical industry shows higher instances of regulatory (n=6 vs. n=13), civil society (n=4 vs. n=6), media (n=3 vs. n=4), and technological captures (n=2 vs. n=3) compared to the pharmaceutical industry. Both industries employ conflict of interests and legitimization strategies to deflect public policy inquiries and protect their interests. However, a notable distinction lies in their objectives. While the analysis of the pharmaceutical industry focuses on profit maximization through biased promotion of health products, the analysis of the chemical sector emphasizes the institutionalization of ignorance, the evasion of liability, and the pre-emption of regulatory actions.
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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 | 0.069 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.106 | 0.064 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".