Nipped in the Bud: How Legal Disparities Create Financial Growth Hurdles in the State-Sanctioned Marijuana Industry and Why Bankruptcy Courts Can Provide a Remedy
Bibliographic record
Abstract
A new marijuana industry has emerged in the United States in the wake of state-by-state legalization of marijuana, and entrepreneurs, investors, and other advisory services are increasingly viewing the marijuana industry as an area of legitimate business opportunity. However, potential investors have been hesitant to establish formal relationships with marijuana businesses that operate legitimately in the eyes of the state but in a cloud of legal uncertainty at the federal level because the Controlled Substances Act criminalizes marijuana. This Note identifies two economic consequences of the conflicts of state and federal law and suggests a temporary solution that would allow states to capture the financial benefits of this industry while the federal government works towards a more permanent, nation-wide solution. The first economic consequence that this Note identifies is that foreign marijuana companies have strategic advantages over U.S. marijuana companies. Investors prefer foreign marijuana companies, particularly those in Canada, instead of the U.S. companies operating in a similar manner. Further, some of these foreign marijuana companies have successfully listed on public U.S. stock exchanges while their domestic counterparts have not been able to, giving foreign competitors greater access to U.S. capital markets than U.S marijuana companies. The second economic consequence this Note discusses is that marijuana companies have been precluded from seeking the protections of bankruptcy law. This Note also suggests that federal bankruptcy courts are equipped to address some of the financial consequences created by this legal disparity. In doing so, they could provide a greater level of comfort to investors and encourage legitimate business development, thus allowing states that have chosen to legalize marijuana to realize the economic benefits of the industry while the federal government navigates the broader issue of federal policy on marijuana.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".