THE LITERATURE ON REFERENCE MANAGEMENT TOOLS: A BIBLIOMETRIC REFLECTION
Bibliographic record
Abstract
The paper aims to provide a bibliometric overview of the literature on reference management tools available on the Web of Science (WoS). Our choice of a bibliometric approach is significant, as it allows us to comprehensively study the literature available on WoS. We utilized a Biblioshiny app to analyze the data retrieved from WoS, further enhancing the depth of our research. The findings revealed that the literature on reference management tools is continuously growing. However, some areas still need to be explored through research. The average citation per document is calculated as 12.7. Bradford's core zone contained eight journals, in which 73 documents were published. Thelwal M, Volkov L P and Dorokhov I N are the most prominent authors publishing on reference management tools. Authorship pattern revealed a trend towards single authorship. The United Kingdom, USA, Netherlands, Canada and Germany were the most cited countries. The top ten highly cited documents shared 43.10% of citations. The conceptual study revealed the leading themes in the literature on reference management tools. The current study is the first direct bibliometric study on reference management tools. It's important to note that our study is limited in that it only covers the literature available in WoS. However, we recognize this limitation and believe that upcoming studies might include Scopus or other indexing databases to provide more generalized results.
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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 | 0.058 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.125 | 0.168 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".