In‐text author citation analysis: Feasibility, benefits, and limitations
Bibliographic record
Abstract
This article explores the feasibility, benefits, and limitations of in‐text author citation analysis and tests how well it works compared with traditional author citation analysis using citation databases. In‐text author citation analysis refers to author‐based citation analysis using in‐text citation data from full‐text articles rather than reference data from citation databases. It has the potential to help with the application of citation analysis to research fields such as the social sciences that are not covered well by citation databases and to support weighted citation and cocitation counting for improved citation analysis results. We found that in‐text author citation analysis can work as well as traditional citation analysis using citation databases for both author ranking and mapping if author name disambiguation is performed properly. Using in‐text citation data without any author name disambiguation, ranking authors by citations is useless, whereas cocitation analysis works well for identifying major specialties and their interrelationships with cautions required for the interpretation of small research areas and some authors' memberships in specialties.
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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 | 0.117 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".