Global Trends in Cannabis and Cannabidiol Research from the Year 1940 to 2019
Bibliographic record
Abstract
Legalization of Cannabis in countries, like Canada, and global demand for non-hallucinating chemical components, such as Cannabidiols (CBD), have stimulated the increased interest from academics, industry, and regulatory agencies. Subsequent research publications in scientific journals in this field are expected to grow rapidly. However, there have been few research reviews that have quantified patterns in research publications concerning cannabis, nor a literature-based perspective on the historical development, current status, and future direction of cannabis research. Here, a bibliometric analysis is performed to address this gap in the scientific literature. A total of 1167 relevant articles (Supplementary file 1) were screened and analyzed using three software tools: HistCite, CiteSpace, and Bibliometric Online Analysis Platform. The performances of relevant countries, institutions, authors, and journals were presented, and the evolutionary trends of different categories were revealed. The historical development of cannabis and CBD research can be clearly divided into three stages, which focus on the chemistry, pharmacology, and molecular biology aspects of Cannabis sativa in general and then a focus on CBD related publications. A timeline was drawn to highlight the major trends in the literature, including scientific discoveries. In the end, several suggestions for future research directions in this field are provided.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.125 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".