How are journals cited? characterizing journal citations by type of
citation
- URL: http://arxiv.org/abs/2102.11043v1
- Date: Mon, 22 Feb 2021 14:15:50 GMT
- Title: How are journals cited? characterizing journal citations by type of
citation
- Authors: Domenic Rosati
- Abstract summary: We present initial results on the statistical characterization of citations to journals based on citation function.
We also present initial results of characterizing the ratio of supports and disputes received by a journal as a potential indicator of quality.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Evaluation of journals for quality is one of the dominant themes of
bibliometrics since journals are the primary venue of vetting and distribution
of scholarship. There are many criticisms of quantifying journal impact with
bibliometrics including disciplinary differences among journals, what source
materials are used, time windows for the inclusion of works to measure, and
skewness of citation distributions (Lariviere & Sugimoto, 2019). However,
despite various attempts to remediate these in newly proposed indicators such
as SJR, SNIP, and Eigenfactor (Walters, 2017) indicators still remain based on
citation counts and fail to acknowledge the critical differences that the type
of citation made, whether it's supporting or disputing a work when quantifying
journal impact. While various programs have been suggested to apply and
encompass citation content analysis within bibliometrics projects, citation
content analysis has not been done at the scale needed in order to supplement
quantitate journal citation analysis until the scite citation index was
produced. Using this citation index containing citation types based on citation
function (supporting, disputing, or mentioning) we present initial results on
the statistical characterization of citations to journals based on citation
function. We also present initial results of characterizing the ratio of
supports and disputes received by a journal as a potential indicator of quality
and show two interesting results: the ratio of supports and disputes do not
correlate with total citations and that the distribution of this ratio is not
skewed showing a normal distribution. We conclude with a proposal for future
research using citation analysis qualified by citation function as well as the
implications of performing bibliometrics tasks such as research evaluation and
information retrieval using citation function.
Related papers
- Distinct citation distributions complicate research evaluations. A single indicator that universally reveals research efficiency cannot be formulated [0.0]
Size-independent, top percentile-based indicators are accurate when the global ranks of local publications fit a power law.
deviations in the least cited papers are frequent in countries and occur in all journals with high impact factors.
arXiv Detail & Related papers (2024-07-12T10:16:21Z) - ALiiCE: Evaluating Positional Fine-grained Citation Generation [54.19617927314975]
We propose ALiiCE, the first automatic evaluation framework for fine-grained citation generation.
Our framework first parses the sentence claim into atomic claims via dependency analysis and then calculates citation quality at the atomic claim level.
We evaluate the positional fine-grained citation generation performance of several Large Language Models on two long-form QA datasets.
arXiv Detail & Related papers (2024-06-19T09:16:14Z) - CausalCite: A Causal Formulation of Paper Citations [80.82622421055734]
CausalCite is a new way to measure the significance of a paper by assessing the causal impact of the paper on its follow-up papers.
It is based on a novel causal inference method, TextMatch, which adapts the traditional matching framework to high-dimensional text embeddings.
We demonstrate the effectiveness of CausalCite on various criteria, such as high correlation with paper impact as reported by scientific experts.
arXiv Detail & Related papers (2023-11-05T23:09:39Z) - Uncited articles and their effect on the concentration of citations [0.0]
Empirical evidence shows that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution.
Are citations becoming more concentrated in a small number of articles? Or have recent geopolitical and technical changes in science led to more decentralized distributions?
This article explores how reference-based and citation-based approaches, uncited articles, citation inflation, the expansion of bibliometric databases, disciplinary differences, and self-citations affect the evolution of citation concentration.
arXiv Detail & Related papers (2023-06-16T15:38:12Z) - Predicting Long-Term Citations from Short-Term Linguistic Influence [20.78217545537925]
A standard measure of the influence of a research paper is the number of times it is cited.
We propose a novel method to quantify linguistic influence in timestamped document collections.
arXiv Detail & Related papers (2022-10-24T22:03:26Z) - Deep Graph Learning for Anomalous Citation Detection [55.81334139806342]
We propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks.
Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts.
arXiv Detail & Related papers (2022-02-23T09:05:28Z) - Towards generating citation sentences for multiple references with
intent control [86.53829532976303]
We build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs.
Experiments demonstrate that the proposed approaches provide much more comprehensive features for generating citation sentences.
arXiv Detail & Related papers (2021-12-02T15:32:24Z) - Cross-Lingual Citations in English Papers: A Large-Scale Analysis of
Prevalence, Usage, and Impact [0.0]
We present an analysis of cross-lingual citations based on over one million English papers.
Among our findings are an increasing rate of citations to publications written in Chinese.
To facilitate further research, we make our collected data and source code publicly available.
arXiv Detail & Related papers (2021-11-07T15:34:02Z) - Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
We redefine the task of scientific papers summarization by utilizing their citation graph.
We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains.
Our model can achieve competitive performance when compared with the pretrained models.
arXiv Detail & Related papers (2021-04-07T11:13:35Z) - Context-Based Quotation Recommendation [60.93257124507105]
We propose a novel context-aware quote recommendation system.
It generates a ranked list of quotable paragraphs and spans of tokens from a given source document.
We conduct experiments on a collection of speech transcripts and associated news articles.
arXiv Detail & Related papers (2020-05-17T17:49:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.