Investing to kill: return on investment of tobacco companies compared to high-mortality and neutral industries
Bibliographic record
Abstract
Terrorists have worked directly with tobacco companies and used tobacco sales to fund traditional terrorist activities. This familiarity with tobacco, coupled with the high mortality rate of tobacco indicates terrorists may use tobacco investing as a means of covert legal killings (CLK), which refers to the terrorist intention of mass killing in a targeted group using legal means. To provide insight into tobacco investors’ motives this study 1) quantifies annual death rate for leading mortality-causing industries in America.; 2) identifies eight companies responsible for the highest CLK; 3) compares investment returns for eight high-mortality companies to the S&P 500 from 2009 to 2019 to determine if tobacco is the most likely target for terrorist-based CLK. The top three highest mortality rate companies and thus best CLK investments from a terrorist perspective were tobacco companies: Altria Group Inc., Reynolds America Inc., and Imperial Brands. Together, these tobacco companies are responsible for >436,800 American premature deaths/year, yet tobacco investments performed worse than the S&P500 over the last decade. It is clear that for CLK investors, tobacco is the most efficient means of investing to kill Americans. Questionable tobacco investor intentionality, combined with the recent advancement in CLK theory makes it reasonable to assume that some tobacco investors are terrorists using their wealth to specifically target and kill Americans. To determine how widespread this practice is, future work is needed to evaluate CLK tobacco investors against the Terrorist Screening Database.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".