Smoke and Mirrors? Disclosures in the Marijuana Industry in Canada
Bibliographic record
Abstract
Abstract Licensed producers (LPs) of marijuana in Canada are embedded in a highly competitive industry where they raise funds from investors to finance their growth. They face substantial risks from the uncertain legal status of marijuana and from its unsettled health and safety consequences. We argue that this context stands to have implications for the disclosures of firms in the marijuana industry. We rely on a multicase study of three large Canadian LPs to explore their mandatory and voluntary disclosures during the third quarter of 2018. We find that their mandatory interim disclosures are largely consistent with disclosure rules that target marijuana operations. We also find that they make voluntary disclosures relevant for their context (e.g., about risks from legal, health, and safety consequences), and that there is variation in these disclosures. We use our findings as a springboard for discussing the antecedents of mandatory and voluntary disclosures in the marijuana industry (i.e., proprietary costs, investor interest, detection costs of selective disclosures), and their consequence (i.e., lack of comparability). We offer suggestions for future research.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".