# The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research

arXiv: https://arxiv.org/abs/2502.20581 · HTML: https://mlsanity.com/paper/2502.20581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [Explaining Relationships Between Scientific Documents](https://mlsanity.com/paper/2002.00317/) (2020)
- [MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting](https://mlsanity.com/paper/2107.00414/) (2021)
- [CORWA: A Citation-Oriented Related Work Annotation Dataset](https://mlsanity.com/paper/2205.03512/) (2022)
- [A review of the literature on citation impact indicators](https://mlsanity.com/paper/1507.02099/) (2015)
- [Modeling Information Change in Science Communication with Semantically Matched Paraphrases](https://mlsanity.com/paper/2210.13001/) (2022)
- [Measuring academic influence: Not all citations are equal](https://mlsanity.com/paper/1501.06587/) (2015)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [Multitask Learning for Citation Purpose Classification](https://mlsanity.com/paper/2106.13275/) (2021)

## Built on by (most cited first, 2 of 2)

- [Research Borderlands: Analysing Writing Across Research Cultures](https://mlsanity.com/paper/2506.00784/) (2025)
- [Counterfactual LLM-based Framework for Measuring Rhetorical Style](https://mlsanity.com/paper/2512.19908/) (2025)

## Abstract (arXiv)

Academic citations are widely used for evaluating research and tracing knowledge flows. Such uses typically rely on raw citation counts and neglect variability in citation types. In particular, citations can vary in their fidelity as original knowledge from cited studies may be paraphrased, summarized, or reinterpreted, possibly wrongly, leading to variation in how much information changes from cited to citing paper. In this study, we introduce a computational pipeline to quantify citation fidelity at scale. Using full texts of papers, the pipeline identifies citations in citing papers and the corresponding claims in cited papers, and applies supervised models to measure fidelity at the sentence level. Analyzing a large-scale multi-disciplinary dataset of approximately 13 million citation sentence pairs, we find that citation fidelity is higher when authors cite papers that are 1) more recent and intellectually close, 2) more accessible, and 3) the first author has a lower H-index and the author team is medium-sized. Using a quasi-experiment, we establish the "telephone effect" - when citing papers have low fidelity to the original claim, future papers that cite the citing paper and the original have lower fidelity to the original. Our work reveals systematic differences in citation fidelity, underscoring the limitations of analyses that rely on citation quantity alone and the potential for distortion of evidence.