Bibliographic record
Abstract
S20: A world less dependent on fossil fuels – scientific evidence and corporate influence. An ISEE Policy Committee Symposium, Room 315, Floor 3, August 26, 2019, 10:30 AM - 12:00 PM The Symposium organised by the Policy Committee of ISEE originated following the widespread surprise and annoyance of our members from the sponsorship of the 2018 ISES-ISEE joint conference in Ottawa by ExxonMobil. ISEE did not directly accept these funds but other societies are more willing to accept them. We will argue that organizations representing health researchers should not accept support from the fossil fuel extraction companies. Banning health research funded by the tobacco industry helped bring major public health gains; we will argue that we should do the same with BigOil. We further argue that ISEE should become more vocal on this issue and promote measures such as divestment from these industries. There are three main reasons for taking this position: (i) the most important is that fossil fuel industries are major determinants of human disease and environmental deterioration; (ii) the second is that they knew! Like the tobacco industry, Big Oil knew for decades that their products could make the planet uninhabitable, and intentionally buried the evidence; (iii) the third reason is that like our stand against the tobacco industry that resulted to significant public health advances, we should take a categorical, effective and clear-cut position against the products and actions of these harmful industries. The science is more than adequate to warrant action. Unless we do this, we will not be able to effectively convince the lay public and our politicians of the urgency with which we must mobilise. The proposed Symposium will illustrate major aspects of health consequences of fossil fuel combustion and the reactions of the industry trying to influence epidemiological research. We will discuss on the way epidemiologists should continue providing essential support to health policies avoiding corporate interests while encouraging industry and other stakeholder involvement as a part of the solution to the problem.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.465 | 0.320 |
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".