Blossoming Insights: A Bibliometric Review of Botanical Gardens’ Research Across Time (1960-2023)
Bibliographic record
Abstract
This bibliometric analysis presents an overview of the literature on botanical gardens.The study identifies research frontiers, themes, and interconnections within the field.It utilizes a dataset of 1340 publications and authors' keyword co-occurrence data, examining trends, patterns, and knowledge gaps from 1960 to 2023.The analysis shows a significant growth in literature, highlighting critical topics such as botanical gardens, conservation, taxonomy, biodiversity, ex-situ conservation, and diversity.Influential researchers such as Sanja Kovačić, Elaissi, and Chemli have directed research toward exsitu conservation and essential oils, reflecting the field's interdisciplinary nature.Leading institutions dominating the discourse include the Chinese Academy of Sciences, the Royal Botanic Gardens UK, and the New York Botanical Garden US.Thematic analyses reveal core themes and emerging topics such as ecological restoration, essential oils, invasive species, and climate change, indicating an expanding scope.Consistent themes like 'botanical gardens' and 'conservation' underscore their enduring significance, while emerging topics like climate change and biodiversity signal shifting research priorities.The growing array of subjects, including 'invasive species' and 'genetic diversity,' reflects the increasing complexity of botanical garden research.This study outlines the developmental trajectory of botanical gardens, guiding future research directions and emphasizing the importance of interdisciplinary collaborations and a comprehensive approach to botanical garden studies.
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 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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.074 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".