A bibliometric analysis of the cannabis and cannabinoid research literature
Bibliographic record
Abstract
BACKGROUND: Cannabis refers to a plant in the family Cannabaceae, which has been used medically, recreationally, and industrially. The last two decades, in particular, have seen a large increase in the volume of literature on this topic. The present bibliometric analysis aims to capture the characteristics of scholarly journal publications on the topic of cannabis and cannabinoid research. METHODS: Searches were run on the Scopus database on April 02, 2021, as follows "(TITLE (cannabi* OR hashish OR marijuana OR marihuana)) AND ( LIMIT-TO ( DOCTYPE,"ar" ) OR LIMIT-TO ( DOCTYPE,"re" ) )". 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 "article" publication type includes publications featuring original research, whereas "review" includes reviews and conference papers. The following data were collected: number of publications (in total and per year), authors, and journals; open access status; publications per journal; journals publishing the highest volume of literature and their impact factors, language of publication; document type; publication country; author affiliations; funding sponsors; most highly cited publications; and most highly published authors. Trends in this subset of publications were identified and presented. Bibliometric networks were constructed 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 was 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 USA (n = 12 420), the UK (n = 2236), and Canada (n = 2062); many of the most common institutional affiliations and funding sponsors originated from these countries. CONCLUSIONS: The number of publications 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 attributed to a large amount of funding dedicated to researching this topic. Future research should continue to investigate changes in the publication characteristics of emerging research, as the volume of publications on this topic is expected to rapidly grow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.022 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.176 | 0.406 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".