This document summarizes research measuring academic influence by analyzing citations. It discusses:
1) Background on citation analysis and its limitations in counting all citations equally;
2) An experiment collecting "influential" citations from papers to build a dataset;
3) Analyzing paper-reference pairs using machine learning classifiers trained on features like citation counts, context similarity, and position;
4) Proposing "influence-primed measures" that weight citations based on frequency to better measure influence, like an influence-primed h-index and impact factor. The researchers conclude influence can be measured by counting more relevant citations.
Durham Leading Research Programme: Academic ImpactJamie Bisset
Aims of the Module
Researchers intending to publish are met with an increasingly complex world of options, influences and pressures. The digital landscape and developments in open access publishing provide additional dissemination channels beyond traditional print; bibliometric tools purport to measure journals’ academic impact ; funder mandates, institutional mandates and routine research assessment exercises place additional requirements on authors which may influence their choice of where and how to publish. The aim of this module is to help researchers navigate this territory and make well- informed decisions.
Content
• Background to the development and use of publication metrics as research indicators, and the issues surrounding this.
• Journal metrics: assess the academic impact of journals, including Journal Impact Factors, Journal Citation Reports and other measures.
• Citations and author metrics: tools available to assess an authors’ individual citation counts and impact, including the h-index.
Approach
The module will take the form of a workshop with on-screen demonstrations and hands-on opportunity, with some presentation and hand-out materials highlighting issues and discussions within the academic community.
Intended outcomes
By the end of the session participants will:
• Increased awareness of the various journal and author metrics available.
• Developed understanding of the key issues around the use of these metrics and what research behaviours might be incentivised.
• Awareness of the potential opportunities for exploring wider academic and non-academic impact of publications from altmetric tools available.
Durham Researcher Development Programme 2015-16: Bibliometric Research Indica...Jamie Bisset
There is an ever-increasing need to make your research more visible as you establish your career, and metrics to measure your research performance when it comes to thinking about promotion and probation.
This session will focus on bibliometric research indicators (such as the Journal Impact Factor and SCImago, author metrics such as the h-index and g-index) and sources for accessing citation data (Web of Science, Journal Citation Reports and Google Scholar). These may be one of several factors to consider when thinking about where to submit an article manuscript for publication to maximise the potential academic impact of the research, and tools useful to be familiar with if they form part of any research evaluation you and your authored journal papers may be subject to.
An additional section will also look at tips to consider when writing an article abstract to maximise its discoverability and cite-ability.
Learning Outcomes:
• Understanding of meaning and intended uses of bibliometric research indicators
• Understanding of how some key indicators (JIF, H-index) are calculated
• Ability to make a judgement as to the appropriateness and limitations of such indicators
• Ability to use online datasets to view and calculate key bibliometric measures
• Awareness of some factors which can increase the visibility and discoverability of your own research in bibliographic databases.
Previous participants have said:
"The session has helped provide me with the basic information on Journal Impact and where to find information such as an author's h-index. It will be useful for future journal submission consideration."
"This session was very useful for me to become familiar with the topic."
Durham Leading Research Programme: Academic ImpactJamie Bisset
Aims of the Module
Researchers intending to publish are met with an increasingly complex world of options, influences and pressures. The digital landscape and developments in open access publishing provide additional dissemination channels beyond traditional print; bibliometric tools purport to measure journals’ academic impact ; funder mandates, institutional mandates and routine research assessment exercises place additional requirements on authors which may influence their choice of where and how to publish. The aim of this module is to help researchers navigate this territory and make well- informed decisions.
Content
• Background to the development and use of publication metrics as research indicators, and the issues surrounding this.
• Journal metrics: assess the academic impact of journals, including Journal Impact Factors, Journal Citation Reports and other measures.
• Citations and author metrics: tools available to assess an authors’ individual citation counts and impact, including the h-index.
Approach
The module will take the form of a workshop with on-screen demonstrations and hands-on opportunity, with some presentation and hand-out materials highlighting issues and discussions within the academic community.
Intended outcomes
By the end of the session participants will:
• Increased awareness of the various journal and author metrics available.
• Developed understanding of the key issues around the use of these metrics and what research behaviours might be incentivised.
• Awareness of the potential opportunities for exploring wider academic and non-academic impact of publications from altmetric tools available.
Durham Researcher Development Programme 2015-16: Bibliometric Research Indica...Jamie Bisset
There is an ever-increasing need to make your research more visible as you establish your career, and metrics to measure your research performance when it comes to thinking about promotion and probation.
This session will focus on bibliometric research indicators (such as the Journal Impact Factor and SCImago, author metrics such as the h-index and g-index) and sources for accessing citation data (Web of Science, Journal Citation Reports and Google Scholar). These may be one of several factors to consider when thinking about where to submit an article manuscript for publication to maximise the potential academic impact of the research, and tools useful to be familiar with if they form part of any research evaluation you and your authored journal papers may be subject to.
An additional section will also look at tips to consider when writing an article abstract to maximise its discoverability and cite-ability.
Learning Outcomes:
• Understanding of meaning and intended uses of bibliometric research indicators
• Understanding of how some key indicators (JIF, H-index) are calculated
• Ability to make a judgement as to the appropriateness and limitations of such indicators
• Ability to use online datasets to view and calculate key bibliometric measures
• Awareness of some factors which can increase the visibility and discoverability of your own research in bibliographic databases.
Previous participants have said:
"The session has helped provide me with the basic information on Journal Impact and where to find information such as an author's h-index. It will be useful for future journal submission consideration."
"This session was very useful for me to become familiar with the topic."
A model for handling overloading of literature review process for social scienceSalam Shah
Literature review is an excruciating part in the process of research. It requires an analysis of
published material on the topic on interest. Moreover, for a new researcher, it is challenging
extract a great number of required objectives, including the problem identification,
no more great deal in this era of Information and Communication Technology (ICT), instead
overloading of the literature is a major problem and the great change to be handle. Often
postgraduate research students raise three questions to their peers and supervisors. First, how
many articles are sufficed for a good literature review? Second, how many past years
literature will be enough to meet the required level for a good literature review? And third,
this research paper a novel hypothetical model is proposed to answer first two questions; the
number of articles required for a good and reasonable literature review and number of years
backward the analysis of articles required for the same. Our results indicate that analysis of
data partially support our hypothetical model and its assumptions.
Keywords: literature review; hypothetical model; load reduction; proposal writing;
information systems.
Review of literature is necessary for research.We can find so many advance thing which are already proven though research by searching the review article only.
This PowerPoint, which was first presented to Indonesian graduate students in Indonesian Embassy Manila last March 1, 2019, aims to describe how to write and publish a research article in reputable international journals and avoid predatory publishing. It describes (1) the major types of paper and their basic structures, (2) the important steps in publishing papers in journals, and (3) the distinction between Web of Science (ISI), Scopus, and predatory journals, and differences between Impact Factor (IF) and Citescore.
Sole reliance on citation data provides an incomplete understanding of research. Although citation analysis may be simple to apply, it should be used with caution to avoid it coming under disrepute through uncritical use. Ideally, citation analysis should be performed to supplement, not replace, a robust system of expert review to determine the actual quality and impact of published research.
Presentation covering introduction to bibliometrics. Suggested audience: PGRs, early career researchers, academic staff wanting refresher, research support staff
A model for handling overloading of literature review process for social scienceSalam Shah
Literature review is an excruciating part in the process of research. It requires an analysis of
published material on the topic on interest. Moreover, for a new researcher, it is challenging
extract a great number of required objectives, including the problem identification,
no more great deal in this era of Information and Communication Technology (ICT), instead
overloading of the literature is a major problem and the great change to be handle. Often
postgraduate research students raise three questions to their peers and supervisors. First, how
many articles are sufficed for a good literature review? Second, how many past years
literature will be enough to meet the required level for a good literature review? And third,
this research paper a novel hypothetical model is proposed to answer first two questions; the
number of articles required for a good and reasonable literature review and number of years
backward the analysis of articles required for the same. Our results indicate that analysis of
data partially support our hypothetical model and its assumptions.
Keywords: literature review; hypothetical model; load reduction; proposal writing;
information systems.
Review of literature is necessary for research.We can find so many advance thing which are already proven though research by searching the review article only.
This PowerPoint, which was first presented to Indonesian graduate students in Indonesian Embassy Manila last March 1, 2019, aims to describe how to write and publish a research article in reputable international journals and avoid predatory publishing. It describes (1) the major types of paper and their basic structures, (2) the important steps in publishing papers in journals, and (3) the distinction between Web of Science (ISI), Scopus, and predatory journals, and differences between Impact Factor (IF) and Citescore.
Sole reliance on citation data provides an incomplete understanding of research. Although citation analysis may be simple to apply, it should be used with caution to avoid it coming under disrepute through uncritical use. Ideally, citation analysis should be performed to supplement, not replace, a robust system of expert review to determine the actual quality and impact of published research.
Presentation covering introduction to bibliometrics. Suggested audience: PGRs, early career researchers, academic staff wanting refresher, research support staff
The presentation deals with variety of tips concerning indexing and citations metrics. These tips will serve as a guideline for researchers for pursuing further research. The main purpose of the presentation is to provide a brief introduction about the indexing metrics. Moreover, it will address the importance of citations, h-index, and how to calculate the h-index for a particular scholar. Furthermore, it will briefly describe how to find an appropriate indexed journal for a specific research article. Eventually, it will concisely demonstrate how to promote a particular research paper across different channels of social media.
How to write a scientific paperGuidelines for the extra credalfredai53p
How to write a scientific paper
Guidelines for the extr
a credit assignment
This
extra credit
assignment is worth 25 points
and is completely voluntary
. All work
must be your own. Any paper containing plagiarism will receive a zero. No late
assignments
will be accepted.
Outline
The topic for this paper is specific to each student and should be acquired from
your instructor.
It should be
in the format of a literature review article and as such should
contain the basic format of a scholarly paper, which i
ncludes a Title, Abstract,
Introduction, Methods, Results and Discussion Section.
I am not requiring all of
these specific sections for this paper. I am simply providing a basic
outline option
. However it is required to have at least two cited articles
and one table/figure
.
Biological Literature
Reference papers must be from a scientific, peer reviewed journal and must be
primary sources (i.e. original findings and ideas). No websites may be used. References
must be full length; this means that they c
ontain an abstract, introduction, methods, and
results/discussion section. Papers may not be cited unless the whole article has been read
directly by you. Reference papers can be accessed through the CBC Library system. Do
not plagiarize the papers you
cite (
It is very easy to check for this, so please
save yourself a lot of heartache and don’t do it
). Also use the papers you cite as
examples of how scientific papers are written, both in terms of format and style.
Title
The title should be short, conc
ise, and informative. The title should be no more
than 45 characters. Below the title
the authors name should appear followed by his/her
department, institution, city and country.
Abstract
The abstract should be 250 words or less and is simply a summar
y of the major
parts in the paper. Usually there is one sentence per paper section: introduction, methods,
results, (including a summary of numerical data), and discussion. The purpose of the
abstract is to give a reader a brief idea of what the paper is
about so that the reader can
determine the relevance to his/her own work.
Introduction
The introduction is the place to present the relevant background context and the
hypothesis. The context should make clear why the hypothesis is interesting and
imp
ortant, and should cite other literature relevant to the research providin
g this rationale.
At the end of your introduction, restate the hypothesis in general terms along with the
purpose of this paper, followed by a series of predictions for each variab
le. For example:
“The purpose of this review was to compare and contrast the prognosis, indications and
contraindications of partial versus total knee arthroplasty in patients who underwent a pre
and/or post operative course of Physical Therapy. I predi
cted that
patients who had a
partial knee replacement, and a course ...
How to write a scientific paperGuidelines for the extra alfredai53p
How to write a scientific paper
Guidelines for the extr
a credit assignment
This
extra credit
assignment is worth 25 points
and is completely voluntary
. All work
must be your own. Any paper containing plagiarism will receive a zero. No late
assignments
will be accepted.
Outline
The topic for this paper is specific to each student and should be acquired from
your instructor.
It should be
in the format of a literature review article and as such should
contain the basic format of a scholarly paper, which i
ncludes a Title, Abstract,
Introduction, Methods, Results and Discussion Section.
I am not requiring all of
these specific sections for this paper. I am simply providing a basic
outline option
. However it is required to have at least two cited articles
and one table/figure
.
Biological Literature
Reference papers must be from a scientific, peer reviewed journal and must be
primary sources (i.e. original findings and ideas). No websites may be used. References
must be full length; this means that they c
ontain an abstract, introduction, methods, and
results/discussion section. Papers may not be cited unless the whole article has been read
directly by you. Reference papers can be accessed through the CBC Library system. Do
not plagiarize the papers you
cite (
It is very easy to check for this, so please
save yourself a lot of heartache and don’t do it
). Also use the papers you cite as
examples of how scientific papers are written, both in terms of format and style.
Title
The title should be short, conc
ise, and informative. The title should be no more
than 45 characters. Below the title
the authors name should appear followed by his/her
department, institution, city and country.
Abstract
The abstract should be 250 words or less and is simply a summar
y of the major
parts in the paper. Usually there is one sentence per paper section: introduction, methods,
results, (including a summary of numerical data), and discussion. The purpose of the
abstract is to give a reader a brief idea of what the paper is
about so that the reader can
determine the relevance to his/her own work.
Introduction
The introduction is the place to present the relevant background context and the
hypothesis. The context should make clear why the hypothesis is interesting and
imp
ortant, and should cite other literature relevant to the research providin
g this rationale.
At the end of your introduction, restate the hypothesis in general terms along with the
purpose of this paper, followed by a series of predictions for each variab
le. For example:
“The purpose of this review was to compare and contrast the prognosis, indications and
contraindications of partial versus total knee arthroplasty in patients who underwent a pre
and/or post operative course of Physical Therapy. I predi
cted that
patients who had a
partial knee replacement, and a course ...
INSTRUCTIONS FOR THE PREPARATION OF A TECHNICAL ESSAY .docxdirkrplav
INSTRUCTIONS FOR THE PREPARATION OF A
TECHNICAL ESSAY
INTRODUCTION
The technical essay is a review paper that synthesizes and interprets work
on a particular subject area. Therefore, the format is not as standardized as that
of a research paper. By bringing together the most pertinent findings of
numerous papers from diverse journals, a review paper serves as a valuable
summary of research. In writing your essay, interpret the primary journal article
in a series of paragraphs that build on your discussion, giving particular attention
to the problem or topic posed in your introduction. In addition, relate your
findings to previous observations or experiments from the supplemental
references that you have chosen. Discuss briefly any logical implications of the
journal articles for practical application or future studies. A good review paper
not only synthesizes information; it also provides a critical overview of an
important scientific problem.
After you have finished your first draft of your essay, review the structure
of your manuscript. Are the sections arranged in logical sequence? After you
are satisfied with the structure of your essay manuscript, attend to the details:
the paragraphs, the sentences, and the words. Expect to do several drafts of
your paper before you are satisfied with the final product. Good writing is
generally the product of careful rewriting or revising in which you evaluate your
attempts at organizing and expressing your ideas. In the process you end up
scrutinizing the ideas themselves, as well as your own mastery of the subject.
CITING REFERENCE MATERIALS
The text of a biological paper usually contains numerous literature
citations, or references, to the published studies of other authors. This is
because scientists rarely work in a vacuum; hypotheses are developed, tested,
and evaluated in the context of what other scientists have written and discovered.
Thus, careful documentation, or acknowledgment of the work of others, is
essential to good scientific writing. Biologists also need to provide literature
citations because, like other writers, they have an ethical and legal obligation to
give credit to others for material that is not their own. Such material includes not
only direct quotations, but also findings or ideas that stem from the work of
someone else.
Unlike writers in the humanities and social sciences, biologists rarely use
footnotes or endnotes to acknowledge sources. Instead, they insert literature
citations directly in the text, either by giving the last name of the author(s) and the
year of publication (name-and-year method), or by referring to each source by a
number method. Such rules, even if they seem arbitrary, make the reporting of
2
references an orderly activity, minimizing confusion for writers, readers, editors,
and publishers.
In this course, the name-and-year method, also known as the Harvard
method,.
This is a guide for an author who wishes to publish an article in our Journal Publication. This is presented by Felix E. Arcilla Jr. at Saint Michael College of Caraga.
Usage-Based vs. Citation-Based Recommenders in a Digital LibraryAndre Vellino
There are two principal data sources for collaborative filtering recommenders in scholarly digital libraries: usage data obtained from harvesting a large, distributed collection of Open URL web logs and citation data obtained from the journal articles.
This study explores the characteristics of recommendations generated by implementations of these two methods: the `bX' system by ExLibris and an experimental citation-based recommender, Sarkanto. Recommendations from each system were compared according to their semantic similarity to the seed article that was used to generate them. Since the full text of the articles was not available for all the recommendations in both systems, the semantic similarity between the seed article and the recommended articles was deemed to be the semantic distance between the journals in which the articles were published. The semantic distance between journals was computed from the ``semantic vectors'' distance between all the terms in the full-text of the available articles in that journal and this study shows that citation-based recommendations are more semantically diverse than usage-based ones.
These recommenders are complementary since most of the time, when one recommender produces recommendations the other does not.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Measuring academic influence: Not all citations are equal
1. Xiaodan Zhu and PeterTurney
National Research Council Canada
Daniel Lemire
TELUQ, Université du Québec Montréal
AndreVellino
School of Information Studies,
University of Ottawa, Ottawa
Measuring Academic Influence:
Not All Citations Are Equal
2. Overview
— Some background in CitationAnalysis
— What we tried to do and why
— How we did it
— What the results were
— What the implications are
3. What is Citation Analysis
Citation analysis refers to the collection of methods for measuring
the importance of scholars, journals and institutions by counting
citations in a graph of references in the published literature.
…
…
… …
…
…
4. Why Do Citation Analysis?
— Reason # 1: Because it generates measurable quantities!
“Since we can’t really measure what
interests us, we begin to be interested
in what we can measure”
JoelWestheimer
Professor of Education
University of Ottawa
5. Uses for Citation Measures
— For Readers
— To evaluate the quality of articles / journals
— For Universities
— To evaluate the productivity of academics
— To help in tenure and promotion decisions
— For Journals
— To attract authors to publish
— For Libraries
— To make collections / acquisition decisions
— To make automated recommendations to users
6. How Are Citations Counted?
— Add 1 for every new occurrence of a cited article
— Sum the results
— Average per article & / or CountTotal # of citations
Problems
— Self citations!
— No measure of quality of citing source
— May be skewed by a small number of highly cited items
— Easy to “game” by tricking Google Scholar
— viz. Ike Inktare h-index = 94 – Einstein h-index = 84
7. h-index
— Jorge Hirsch (PNAS, 2005) defined the h-index:
— Attempts to measure both the productivity and impact of the
author’s published work
— An author has index h if h of their N papers have at least h citations
each, and the other (N − h) papers have at most h citations each.
8. Some Criticisms of the h-index
— The h-index does not account for the number of authors or the order of
the authors of a paper.
— Cannot use the h-index to compare authors in different fields
— Young researchers with as yet short careers are at a built-in disadvantage
over older researchers
— Constrained by the total number of publications
— 10 papers each w/ 100 citations each = 10 papers w/ 10 citation each
“[h-index] captures a small amount of information
about the distribution of a scientist's citations [and] loses crucial
information that is essential for the assessment of research.”
Adler, R., Ewing, J.Taylor, P. Citation statistics.
A report from the International Mathematical Union.
http://www.mathunion.org/fileadmin/IMU/Report/CitationStatistics.pdf
9. Journal Impact factor (IF)
— Invented by Eugene Garfield in 1955 to identify journals for
Science Citation Index
— Definition:
Total Citations (2 preceding years )
Total Articles (2 preceding years )
=JIF
i.e. the impact factor of a journal is the average number
of citations to those papers that were published during
the two preceding years
¨ e.g. the number of times articles published in 2001 and 2002
were cited by indexed journals during 2003 / the total number
of items published in 2001 and 2002
10. Some Criticisms of Impact Factor
— Letters or editorials in some journals (e.g. Nature) are often cited
(and counted) in “Total Citations” (numerator) but not in “Total
Articles”
— 2-year window not applicable in many fields (e.g. in Math 90% of
citations fall outside the 2-year window)
— IF varies considerably across disciplines (Math has an average of
0.9 citation per article, Life Sciences have an average of 6.2)
“Using the impact factor alone to judge a journal is
like using weight alone to judge a person's health.”
Adler, R., Ewing, J.Taylor, P. Citation statistics.
A report from the International Mathematical Union.
http://www.mathunion.org/fileadmin/IMU/Report/CitationStatistics.pdf
12. — As early as 1965 Garfield identified 15 different reasons for
citing
— giving credit for related work
— correcting a work
— criticizing previous work
— Many attempts since to categorize citations
One Big Assumption
All citations should count equally!
13. Citation Typing Ontology (CiTO)
Here are first 21 of the 91 citation types in CiTO
http://imageweb.zoo.ox.ac.uk/pub/2008/plospaper/latest/#refs
Example of semantically annotated article using CiTO:
14. Our Objective
— Solve a binary classification problem:
Given a Paper-Reference (P-R) pair, does
P-R belong to the class “R is highly
influential for P” or not.
Our Method
— Apply Machine Learning methods to train a computer to
recognize “Highly Influential Reference” from examples
15. Step 1 – Data Collection
We believe that most papers are based on 1, 2, 3 or 4
essential references. By an essential reference, we mean a
reference that was highly influential or inspirational for the
core ideas in your paper; that is, a reference that inspired or
strongly influenced your new algorithm, your experimental
design, or your choice of a research problem. Other
references merely support the work.
16. We asked for
— Title of your paper (research papers only; no surveys)
— The essential references does your paper build?
We got
— 100 papers
— 322 “influential” references
— i.e. 3.2 “influential references” per article
— Each paper
— Contained ~ 31 references in the References section
— Cited ~ 54 references in the body of the paper
— i.e. each reverence was cited an average of 1.7 times per paper
17. The Problem
— The 100 papers yield 3143 paper-reference pairs
— The authors have selected ~320 paper-reference pairs
— Algorithmically: to accurately select those 320 from the 3142
18. Paper – Reference Analysis
— OpenNLP used to detect sentence boundaries and tokenize.
— ParsCit to parse the papers.
— ParsCit is an open-source package for parsing references and
document structure in scientific papers.
— Regular expressions to capture citation occurrences in paper
bodies that were not detected by ParsCit.
20. We Looked at 5 Classes of Features
1. Count-based features
2. Similarity-based features
3. Context-based features
4. Position-based features
5. Miscellaneous features
21. Count Based Features
— Total number of times a paper is referenced in the citing paper
— The number of different sections in which a given reference appears
— Number of times a paper is referenced in the
— “Related” section
— “Introduction” section
— “Core” sections (all sections excluding “Related”,“Introduction”,
“Acknowledgements”,“Conclusion” and “FutureWork”
— The number of different sections in which a reference appears
23. Citing Context
— When an article is cited, the linguistic context in which the
article is cited is considered as saying something about the
cited article.
e.g.
“Like Moravcsik and Murugesan (1975),we are concerned
about the side effects of counting insignificant references”
25. Other Context Based Features
— Authors explicitly mentioned in citation context?
— Citation alone [4] or with others [3,4,5]
— If “with others” is it first? (e.g.“[3]” is first in “[3,4,5]”)
Using pre-defined word-lists, is the lexical content of a citation
— “relevant” [likewise,influential,inspiring useful….]
— “new” [recently,latest,current,improved…]
— “extreme” [greatly,intensely,acutely,almighty,awfully]
— “comparative” [easy,easier,easiest,strong,stronger…]
26. Lexical Context Features
Using a lexicon of 114,271 words obtained from the General
Inquirer Lexicon (11,788 words) extended w/Wordnet +
Turney and LittmanAlgorithm,
— Count the number of words labeled
— “Strong”
— “Positive”
— “Evaluative”
Also, sentiment analysis with a different lexicon gave us
— Presence / absence of “Emotion” (Joy, Sadness,Anger, Fear, etc.)
— “Positive” / “Negative”
27. Position Based Features
Where does the citation occur?
— Citation appears at the beginning of a sentence? (Y/ N)
— Citation appears at the end of a sentence? (Y/N)
— Where are the sentence(s) in which the citation(s) occur(s)
e.g.
— 0 (First sentence) to 1 (Last sentence)
— distance from the mean of occurrences of all citations
29. Top 7 Features: 4 “counts”, 3 “similarity”
Counts in Paper
Counts in Sections
Counts in Core Section
Title-Abstract Similarity
Counts in Intro Section
Title-Core Similarity
Title-Intro Similarity
32. hip-index
— Each occurrence of a citation of paper R by paper C = 1
— hip-index (h-influence-primed) index for an author is the
largest number h such that at least h of the author's papers
have an influence-primed citation count of at least h.
33. Examples
hip-index = 5
h-index = 2
cited 3 times by C1 = 9
cited 2 times by C2 = 4
cited 2 times by C3 = 4
cited 2 times by C4 = 4
R3 – cited 3 times by C5 = 9
R4 – cited 3 times by C6 = 9
R5 – cited 3 times by C7 = 9
R6 – cited 2 times by C8 = 4
R7 – cited 1 times by C9 = 1
13
8
9
9
9
4
1
hip-index = 3
h-index = 2
cited 2 times by C1 = 4
cited 1 times by C2 = 1
cited 2 times by C3 = 4
cited 1 times by C4 = 1
R3 – cited 2 times by C5 = 4
R4 – cited 1 times by C6 = 1
R5 – cited 1 times by C7 = 1
R6 – cited 1 times by C8 = 1
R7 – cited 1 times by C9 = 1
5
5
4
1
1
1
1
R1
R2
R1
R2
34. Using hip-index to Predict ACM Fellows
— Used the citation network constructed from
— ~ 20,000 papers in theAssociation for Computational Linguistics
Anthology
— Calculated the h-index ofACL Fellows
— Calculated the hip-index ofACL Fellows
— Compared the precision of h-index and hip-index
— the number ofACL Fellows in the top N divided by N
36. Conclusions
— We can throw away h-index and Impact Factor etc. completely
OR we can try to improve them by counting citations more
relevantly
— A measure of academic influence for a citation is possible and
— It is easy to compute to a first approximation – merely count
their frequency
— Apply the influence-primed weights on citation graphs to
compute
— Influence-primed Impact Factor, g-index etc.