How Do Consumers Describe Cannabis? Using a Sorting Task to Create a Lexicon to Describe Cannabis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it