Re: Sponsorship by Big Oil, Like the Tobacco Industry, Should Be Banned by the Research Community
Bibliographic record
Abstract
To the Editor: The provocative editorial by Kogevinas and Takaro1 suggests that the International Society for Environmental Epidemiology (ISEE) has embarked on a slippery slope of advocacy and selective indignation. Kogevinas and Takaro1 were apparently displeased by Big Oil companies’ sponsorship of the ISEE/International Society of Exposure Science (ISES) joint conference held in August 2018 in Ottawa, Canada. In their editorial, they point to ISES as being responsible and propose that we in the field of Environmental and Occupational Health should no longer accept research funding from Big Oil companies. One expects that Drs. Kogevinas and Takaro1 are aware that their own organization is being sponsored by one of the most notorious e-commerce employers on our planet: Amazon. Within the November 2018 ISEE newsletter,2 the following suggestion is made: “As the holiday season approaches, remember that you can donate 0.5% of your Amazon purchase price to ISEE. To take advantage of this program, simply go to smile.amazon.com and register to support “International Society for Environmental Epidemiology” before you make your purchases. These donations will go into ISEE’s general account and will be used to improve the services offered by the society.” Amazon’s reputation for poor treatment of its own workers should be familiar to every ISEE member who reads newspapers.3,4 Objecting to sponsorship from Big Oil while accepting sponsorship from Amazon may point to inconsistent standards. Does the Kogevinas and Takaro1 editorial perhaps imply that we should only accept governmental funding, or are some governments more acceptable than others? For example, is it acceptable for ISEE to take issue with the UralAsbest Cohort study carried out by the International Agency for Research on Cancer but funded by the Russian Federation?5 In that case, shouldn’t ISEE also provide its members with a list of countries from which the receipt of research money is not acceptable? My opinion is that ISEE should not wade into the murky waters of politics and advocacy. Instead, I would advise ISEE and ISES members to secure funding from any source, as long as they are able to guarantee total independence and scientific integrity through firewalls and independent scientific advisory committees. There is no need for ISEE or its ethics committee to have the last say in what is acceptable or not. Furthermore, close collaboration with governments, industries, nongovernmental organizations, employers (associations), unions, and individual workers is a must, because within our field of public health what ultimately counts is to “Ensure healthy lives and promote well-being for all at all ages” by 2030.6 That is closer than we think. Hans KromhoutInstitute for Risk Assessment SciencesUtrecht University, UtrechtThe Netherlands[email protected]
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.031 | 0.033 |
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".