Black Market to Blue Chip: \nThe Futures of the Cannabis Industry in Canada
Bibliographic record
Abstract
The world is changing its mind on cannabis, and we are not in the same world we were in 1923 when Canada first made marijuana illegal to smoke. Culture, technology, knowledge, and people have changed. We have a lot more research, seen first-hand how cannabis can help and hurt people, and heard other’s similar and different stories. How do we apply what we’ve learned about strategy, business, and design to turn a prohibition policy into a regulatory framework? \n \nLegalization of cannabis in Canada has been delayed to after August, if all things go smoothly and there are no additional setbacks. Legalization creates a unique opportunity for Canada to become a global leader in innovation, policy, research, and regulation. While there will be many changes happening simultaneously in the future, the principle domains of inquiry for this research project will be understanding the systems connections and impact of scientific research, technological innovation, regulatory frameworks, short- and long-term economic growth strategies, agricultural and environmental regulations, and cultural change strategies on the possible futures of the cannabis industry in Canada. \n \nDesigning the policy that will create the boundaries for such a complex system means that we need to know all the different moving parts. By understanding the current environmental context and the changes in these systems, a greater understanding of the complexity of connections and the different future scenarios of cannabis legalization can be achieved. These scenarios will provide policy makers, educators, and entrepreneurs with plausible futures that they can use to develop and test drive short- and long-term strategies through, in hopes of turning Canada into a global leader in cannabis and policy innovation. \n \nThe Government of Canada assembled a Taskforce to advise the government on how best to move forward. Within their report, the taskforce identified nine public policy objectives. It’s safe to say that any system that has nine policy objectives is not a simple task and that solution will not happen overnight. In order to protect Canada’s youth from the black market, a system that is accessible, resilient, sustainable, and profitable must take its place. If it is not accessible, people will go back to the black market. If it is not resilient or sustainable, it will not outpace the black market. If it is not profitable, it will not attract participants that are willing to be regulated. If we do not eradicate the black market, they will continue to sell to youth. \n \nCanada should be considering more than two goals, reducing underage access and keeping profits out of the hands of criminals, by casting a wider set of goals that cover more of the legalization ecosystem. By putting environmental restrictions and constraints around cannabis production, Canada can push for innovation in a nascent market. Cannabis agri-tech innovations could then be applied to broader social issues such as food insecurity and climate change. In short, Canada needs to be more ambitious and consider that they are not simply legalizing a plant. They have the opportunity to take a resilient underground market and flip it into an innovation powerhouse.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".