Comparing two cannabis tax-base systems: Lessons learned from legal cannabis products sold in Ontario, Canada.
Bibliographic record
Abstract
Aims To examine the price and tax structures of cannabis products sold in Ontario to inform the government on how to improve the policy on the cannabis tax base from a public health standpoint. Design Economic evaluations to assess price and tax structures of various cannabis products Setting Ontario Participants Data of cannabis products sold in Ontario regarding the Ontario Cannabis Store (OCS)’s buy and sell prices and product characteristics, including the product’s type, quantity per package, and quantity of THC and CBD. The OCS provided data on 2,601 units of cannabis products in March 2022. Measures Measures examined include the harmonized sales tax, the seller’s markup, the producer’s price, the flat-rate tax, ad valorem tax, effective excise tax, provincial adjustment tax, and excise tax per mg THC. The percentage of each tax compared to the retail price is also calculated. Findings A flat-rate tax of $1 per gm of 10% THC flower produces a tax of $0.01 per mg THC. Taxing cannabis products based on gm of dried flower results in high-THC products being taxed at a lower rate. Evidence showed that product categories with types and potencies that had lower taxes rates per mg THC imposed upon them had more product varieties. Conclusions The Ontario government should consider changing the tax base from the current one based on gm of flower to one based on mg of THC. Moreover, indexing the flat-rate tax to inflation is important to prevent the tax’s flat rate from becoming lower over time.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".