Counting first, last, or all authors in citation analysis: A comprehensive comparison in the highly collaborative stem cell research field
Bibliographic record
Abstract
Abstract How can citation analysis take into account the highly collaborative nature and unique research and publication culture of biomedical research fields? This study explores this question by introducing last‐author citation counting and comparing it with traditional first‐author counting and theoretically optimal all‐author counting in the stem cell research field for the years 2004–2009. For citation ranking, last‐author counting, which is directly supported by Scopus but not by ISI databases, appears to approximate all‐author counting quite well in a field where heads of research labs are traditionally listed as last authors; however, first author counting does not. For field mapping, we find that author co‐citation analyses based on different counting methods all produce similar overall intellectual structures of a research field, but detailed structures and minor specialties revealed differ to various degrees and thus require great caution to interpret. This is true especially when authors are selected into the analysis based on citedness, because author selection is found to have a greater effect on mapping results than does choice of co‐citation counting method. Findings are based on a comprehensive, high‐quality dataset extracted in several steps from PubMed and Scopus and subjected to automatic reference and author name disambiguation.
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 | 0.035 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.027 | 0.302 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".