All‐author vs. first‐author co‐citation analysis of the Information Science field using Scopus
Bibliographic record
Abstract
Abstract Although many studies on the various ways of allocating credit among co‐authors have brought into general recognition that different citation counting methods can result in quite different author rankings, studies on different author co‐citation counting methods are still largely missing. This paper examines whether different co‐citation counting methods produce different results in author co‐citation analysis studies of the intellectual structure of research fields, and if so, in what ways they differ. Our results indicate that, with respect to the major specialties and how they relate to each other, the intellectual structures of the Information Science field identified through author co‐citation analyses based on different co‐citation counting methods are largely equivalent, but when it comes to detailed structure, results differ in a number of ways. In particular, classic first‐author co‐citation analysis appears to better represent the theoretical and methodological aspects of the field whereas all‐author co‐citation analysis favors more recent empirical studies, and picks out some tightly collaborative research groups or projects. We experiment with using meaningful diagonal values in a co‐citation matrix rather than using statistically generated values, and observe favorable results. We also employ a new visualization technique for reporting the results of a classic author co‐citation analysis, using a bipartite graph that represents all the information in the factor matrix of specialties and author loadings as author and factor vertices connected by edges with loadings as similarity‐measure line values, laid out algorithmically in two dimensions.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".