What a legacy: a call to action to facilitate entry into the licensed cannabis market in Canada
Bibliographic record
Abstract
Purpose Almost five years after legalization, the unlicensed cannabis market is still thriving in Canada, and legacy cannabis retailers continue to face barriers to legal market entry. This study aims to shed light on these challenges and offer policy recommendations supporting legacy retailers and the government’s goals of enhancing public safety and displacing the unlicensed market. Design/methodology/approach This study reviewed online sources, including the media, gray literature, government, and other policy and legal websites, to identify legacy retailers’ challenges to entering the Canadian ecosystem since legalization and policy approaches of legalized jurisdictions with similar issues. Findings Legacy retailers face financial, legal and social barriers to entering the legal market. The Canadian government should focus on lowering and eliminating these barriers by developing programs that reduce financial risks and required capital, facilitate partnership programs and accelerators, provide innovative options that reduce overhead expenses, encourage pooled ownership to support small businesses, prioritize market entry for equity-deserving individuals and enable automatic expungement. A description of programs that have been implemented in other jurisdictions to address similar barriers is provided. Practical implications The policy recommendations in this paper would enable increased entrepreneurship and employment in a growing sector. While the tax revenue earned from the new market entrants may not be enough to support all the recommended policy initiatives, it could be reinvested to fund some of them creating sustainable growth opportunities. Originality/value The paper provides practical, timely policy recommendations on expanding the legal cannabis market in Canada and addressing unintended negative consequences of current policies.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".