Like birds of a feather? Network analysis on the connectedness between the cannabis, tobacco, alcohol and pharmaceutical industries
Bibliographic record
Abstract
Despite legalizing cannabis in Canada, Uruguay and parts of the United States, the debate on legalizing cannabis is still ongoing. Several countries opted for a commercial model regarding cannabis regulation. Research showed that the legalization of both medical and recreational cannabis directly affects the sales of alcohol, tobacco and medicines, each in a different manner. Therefore, the alcohol, tobacco and pharmaceutical companies have vested interests in the upcoming cannabis industry. Legalization of cannabis can be seen as both a threat or an opportunity to the other industries. To study this relationship, a network analysis was conducted. Annual reports, newsletters, etc. of various cannabis companies were studied. This analysis revealed that the tobacco, alcohol and pharmaceutical industry try to take advantage of the upcoming cannabis industry to keep their sales and revenue as high as possible. They do this by introducing new products, but also by collaborations and large investments into the cannabis industry. Next to the network analysis, the flow of employees between the industries was researched. It became clear that in the top management positions of the cannabis companies, most of the employees had a professional history in the alcohol, tobacco or pharmaceutical industry. It can therefore be assumed that much knowledge and practices flow from these industries into the cannabis industry. As such, we conclude that these mature industries consider the legalization of cannabis as an opportunity they aim to take advantage of.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.070 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".