How authors cite references? A study of characteristics of in‐text citations
Bibliographic record
Abstract
ABSTRACT How to differentiate citations is continuously an important research question of citation analysis. Recently, some researchers analyze full‐text academic articles with tools and techniques of natural language processing to find out the characteristics of in‐text citations. In this study, we analyze 4,255 articles published during 2007 to 2016 to explore how authors of library and information science (LIS) cite references in their articles. The pattern of citations shows that LIS authors cite more works on average but the average in‐text citation times per reference in an article only increase slightly in this period. There are more in‐text citations in the first quarter fraction of an article and the number of in‐text citations decrease gradually with text progression. The distribution of POS shows that when LIS authors mention references in text body, they use more noun, verb, and preposition than less adjective and adverb. We also choose the high frequency words of different POS to investigate which words authors prefer in in‐text citations. In these high frequency words, nouns tend to be about research topics. Verbs are neutral or used to describe methods or findings of these cited works. As to adjective and adverb, most of them are positive or neutral. But there are more negative words in adverb.
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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 | 0.006 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".