Author bibliographic coupling: Another approach to citation‐based author knowledge network analysis
Bibliographic record
Abstract
Abstract While bibliographic coupling (BC) as a measure of relatedness between documents was proposed a full decade before co‐citation, interest in applying BC to mapping the intellectual structure of research areas has only recently resurged, perhaps because it allows researchers to circumvent problems of the so far dominant co‐citation analysis. Especially for mapping the intellectual structure of a research field as represented by its authors, author co‐citation analysis (ACA) has frequently been applied over the last two decades, but no author BC analysis has so far been attempted. In this paper, we define author BC and conduct an author BC analysis of the Information Science field using the same dataset as that used in our previous ACA study, which covers Information Science during 1996‐2005. We find that these two citation‐based author knowledge network analysis methods complement each other, with one providing a more realistic picture of the state of research within the IS field and the other revealing the structure of both internal and external influences on the IS research. In combination, the two methods provide a more comprehensive view of the intellectual structure of the IS field than either of them alone.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".