A Bibliometric Analysis of the Cannabis and Cannabinoid Research Literature
Bibliographic record
Abstract
Abstract Background: Cannabis refers to a flowering plant in the family Cannabaceae, which has been used medically, recreationally, and industrially. The history of cannabis has been both long and complex, however, the last few decades have seen a large increase in the volume of literature on this topic. The objective of the present bibliometric analysis is to capture the characteristics of peer-reviewed publications on the topic of cannabis and cannabinoid research.Methods: Searches were run on April 02, 2021, and results were exported on the same day to prevent discrepancies between daily database updates. Only “article” and “review” publication types were included; no further search limits were applied. The following bibliometric data were collected: number of publications (in total and per year), authors and journals; open access status; journals publishing the highest volume of literature and their impact factors; language, countries, institutional affiliations, and funding sponsors of publications; most productive authors; and most highly-cited publications. Trends associated with this subset of publications were identified and presented. Bibliometric networks were constructed and visualized using the software tool VOSviewer.Results: A total of 29 802 publications (10 214 open access), published by 65 109 authors were published in 5474 journals from 1829 to 2021. The greatest number of publications were published over the last 20 years. The journal that published the largest number of publications was Drug and Alcohol Dependence (n=705). The most productive countries included the United States (n= 12 420), the United Kingdom (n=2236), and Canada (n=2062); many of the most common intuitional affiliations and funding sponsors also originated from these three countries.Conclusions: The number of publications collectively published on the topic of cannabis follows an upward trend. Over the past 20 years, the volume of cannabis research has grown steeply, which can be largely attributed to the existence of a large amount of funding dedicated to research this topic. Future research should continue to investigate changes in the publication characteristics of emerging cannabis research, especially as it is expected that the body of publications on this topic is expected to rapidly grow.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.202 | 0.217 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".