A scientometric analysis and visualization of the 50 highly cited papers of Eugene Garfield
Bibliographic record
Abstract
Eugene Garfield’s contributions to global informetrics and scientometrics literature is significant. In this paper, a scientometric analysis of Eugene Garfield’s 50 highly cited papers is performed. His papers were published in 32 journals including top-ranked journals such as Nature and Science. The top 15 keywords with the strongest citation bursts from 1989 to 2009 and references with strong citation bursts are presented. Co-citation analysis and bibliographic coupling analysis based on source journals using VOSviewer were carried out. The result revealed that keywords 'citation relationship', 'scientific journals', 'biological journal’ and 'self-citations' started to burst/hotspot in 2002. The term 'citation analysis' has the highest number of four years' popularity as citation burst. The study further revealed that the top 50 publications of Eugene Garfield gained 8441 citations of the total citations of 9121 from 254 published documents. Garfield has Total Link Strength of 35 and has received 8511 citations which comes to 93.31% of the total citations and proved his dominance over the collaborators. Ninety percent of the papers (45) published in the USA and above 92% of the citations (8419) were also received from the USA's publications. Just five papers in three journals received 4856 citations (53.23%) of the total 9121 citations. These three journals include three papers in Science (with 3027); one each in Journal of the American Medical Association (with 1323 citations) and The Canadian Medical Association Journal (with 606 citations).
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.075 | 0.079 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".