Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic‐coupling analysis
Bibliographic record
Abstract
Abstract Author cocitation analysis (ACA) has frequently been applied over the last two decades for mapping the intellectual structure of a research field as represented by its authors. However, what is mapped in ACA is actually the structure of intellectual influences on a research field as perceived by its active authors. In this exploratory paper, by contrast, we introduce author bibliographic‐coupling analysis (ABCA) as a method to map the research activities of active authors themselves for a more realistic picture of the current state of research in a field. We choose the information science (IS) field and study its intellectual structure both in terms of current research activities as seen from ABCA and in terms of intellectual influences on its research as shown from ACA. We examine how these two aspects of the intellectual structure of the IS field are related, and how they both developed during the “first decade of the Web,” 1996–2005. We find that these two citation‐based author‐mapping methods complement each other, and that, in combination, they provide a more comprehensive view of the intellectual structure of the IS field than either of them can provide on its own.
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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.061 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".