How Do Consumers Describe Cannabis? Using a Sorting Task to Create a Lexicon to Describe Cannabis
Bibliographic record
Abstract
ABSTRACT Cannabis consumers' preference while selecting cannabis products, specifically dried flower, has been undergoing a drastic change as more consumers have begun considering the impact that flavor has on their purchasing intent of different cannabis species (including Indica, Sativa, and or hybrid varieties). As such, the objective of this study was to quantify consumers' sensory perceptions of cannabis strains currently on the market. The researchers used Natural Language Processing (NLP) and online North American cannabis retailers, cannabis user reviews, and other informative cannabis websites to identify 107 different descriptors. Cannabis consumers (n = 123) were asked to complete a free word sorting task on the 107 most frequently cited sensory descriptors identified using NLP, as well as identify which attributes they associated with high and low‐quality cannabis. The consumers sorted the descriptors into 10 different categories (fruit, berry/dried fruit, savory, floral, spices, spicy, potent, smoke, roasted, and confectionary). As the cannabis market continues to grow and mature in North America, this study presents a baseline of how consumers describe different cannabis varieties.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".