A Scientometric Analysis of Cannabis sativa Research
Bibliographic record
Abstract
Cannabis sativa, a diploid plant from the Cannabaceae family, was traditionally cultivated for food, medicine, fiber, and industrial purposes. Pharmacologically, this plant is significant due to cannabinoid activity, especially in medicine, nutrition, and industry. Despite its diverse applications, C. sativa has faced legal challenges that have severely impeded research in the area and commercial applications. Scientometric analysis, which is the quantitative assessment of scientific literature, is the means employed in a systematic way to gather and understand how each field is moving, who is contributing more, and what subject matter emanates therefrom. This study aimed to analyze the last 24 years of global research trends on C. sativa using Scopus and PubMed databases. The methodology employed several advanced bibliometric analyses using VOS-viewer software. Our results show that a sharp growth has been observed in publication output, mainly after 2015 and peaking in 2024. Most studies revolved around medical, pharmacological, and neuroscientific fields. The principal authors were Raphael Mechoulam, and the institutions such as the University of Toronto and Harvard Medical School featuring strongly. The latter was also noted to provide major funds, along with The National Institutes of Health (NIH) and probably the major U.S. agencies. Nevertheless, keyword analysis revealed that dominant themes were medical cannabis, legalization, chronic pain, and cannabinoid pharmacology, while epigenotoxicity and genotoxicity came up as emerging areas. Our study concludes that C. sativa research becomes increasingly interdisciplinary and internationally collaborative, due to evolving legal frameworks and growing medical interest. Future research should bridge disciplinary silos, address under-refined areas such as environmental sustainability, and incorporate altimetric and policy data. Scientometric mapping hence yields actionable insight into scholarly and policy priorities in the developing field of C. sativa studies.
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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 | 0.017 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.168 | 0.244 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".