Ethics, morality, and conflicting interests: how questionable professional integrity in some scientists supports global corporate influence in public health
Bibliographic record
Abstract
Clinical and public health research, education, and medical practice are vulnerable to influence by corporate interests driven by the for-profit motive. Developments over the last 10 years have shown that transparency and self-reporting of corporate ties do not always mitigate bias. In this article, we provide examples of how sound scientific reasoning and evidence-gathering are undermined through compromised scientific enquiry resulting in misleading science, decision-making, and policy intervention. Various medical disciplines provide reference literature essential for informing public, environmental, and occupational health policy. Published literature impacts clinical and laboratory methods, the validity of respective clinical guidelines, and the development and implementation of public health regulations. Said literature is also used in expert testimony related to resolving tort actions on work-related illnesses and environmental risks. We call for increased sensitivity, full transparency, and the implementation of effective ethical and professional praxis rules at all relevant regulatory levels to rout out inappropriate corporate influence in science. This is needed because influencing the integrity of scientists who engage in such activities cannot be depended upon.
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 | 0.143 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.157 |
| Scholarly communication | 0.035 | 0.020 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.017 | 0.021 |
| 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".