---
title: "Measuring the Evolution of a Scientific Field — Jurgens 2018"
description: "Citation counts usually treat every reference as equal, but authors cite earlier work for very different purposes."
authors: ["David Jurgens", "Srijan Kumar", "Raine Hoover", "Dan McFarland", "Dan Jurafsky"]
year: 2018
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q18-1028/"
doi: "10.1162/tacl_a_00028"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/jurgens-2018-measuring-evolution-scientific-field-through/"
---
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# Measuring the Evolution of a Scientific Field through Citation Frames

**Authors:** David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, [Dan Jurafsky](https://md.research.elamur.ai/free/en/authors/dan-jurafsky/) · **Year:** 2018 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q18-1028/) · [PDF](https://aclanthology.org/Q18-1028.pdf) · [DOI 10.1162/tacl\_a\_00028](https://doi.org/10.1162/tacl_a_00028) · [OpenAlex](https://openalex.org/W2808556605)

**Citations:** 210 citations (openalex-snapshot · 2026-10-14) · [101](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Citation counts usually treat every reference as equal, but authors cite earlier work for very different purposes. Before this paper, those purposes had only been studied by hand in small samples. Jurgens and colleagues study citation function across an entire discipline, NLP. They merge earlier taxonomies into six classes: BACKGROUND, MOTIVATION, USES, EXTENSION, COMPARISON OR CONTRAST and FUTURE. With this scheme they annotate 1,969 citation contexts from the ACL Anthology Reference Corpus (ARC). The data trains a state-of-the-art classifier that combines several feature types: where a citation sits in the paper, lexical and grammatical patterns, field-level metadata, and new unsupervised features drawn from topic models and argument structure. The classifier then labels over 134,000 citations in more than 20,000 papers covering almost forty years. The results show that citation types track a paper's discourse structure but differ by venue, and that a paper's framing helps forecast its later citation count. Over time, NLP authors compare less and reuse a shared core of methods, which signals rising consensus. The released data and code support large-scale studies of how science develops.

## Key points
- **Dataset:** 1,969 annotated citations in total.
  - 1,436 come from 52 fully labeled ARC papers.
  - 533 come from 133 further papers, gathered by keyword-targeted sampling of rare classes.
  - BACKGROUND is by far the largest class (1,021), and FUTURE is the smallest (68).
- **Annotation pipeline:**
  - Citations and their references were extracted with ParsCit.
  - Each instance was labeled by two NLP-trained annotators in Brat.
  - All disagreements were fully adjudicated.
- **Field-scale labeling:** 134,127 citations from over 20,000 NLP papers were classified automatically.
- **Consensus trend:** COMPARISON OR CONTRAST citations decline steadily (r = −0.899), and non-methodological citations decline overall (r = −0.663). Comparisons and uses also concentrate on a smaller set of works. One exception is a small rise after ACL venues allowed unlimited references (ACL in 2010, NAACL in 2011).
- **Resources:** The data and code are released at github.com/davidjurgens/citation-function.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

101 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 95 not yet.

- [SciBERT: A Pretrained Language Model for Scientific Text](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://md.research.elamur.ai/free/en/papers/cohan-2019-structural-scaffolds-citation-intent-classification/)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://md.research.elamur.ai/free/en/papers/jain-2020-scirex-challenge-dataset-document-level/)
- [S2ORC: The Semantic Scholar Open Research Corpus](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/)
- [Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/)
- [Large Language Models for Citation Function Classification](https://md.research.elamur.ai/free/en/papers/vodicka-2026-large-language-models-citation-function/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{jurgens2018measuring,
  title = {{Measuring the Evolution of a Scientific Field through Citation Frames}},
  author = {Jurgens, David and Kumar, Srijan and Hoover, Raine and McFarland, Dan and Jurafsky, Dan},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2018},
  doi = {10.1162/tacl\_a\_00028},
  url = {https://doi.org/10.1162/tacl_a_00028}
}
```

- [← previous MCDTB: A Macro-level Chinese Discourse TreeBank](https://md.research.elamur.ai/free/en/papers/jiang-2018-mcdtb-macro-level-chinese-discourse/)
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- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/)

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