10,920 research outputs found
Markets for Reputation: Evidence on Quality and Quantity in Academe
We develop a theory of the market for individual reputation, an indicator of regard by oneâs peers and others. The central questions are: 1) Does the quantity of exposures raise reputation independent of their quality? and 2) Assuming that overall quality matters for reputation, does the quality of an individualâs most important exposure have an extra effect on reputation? Using evidence for academic economists, we find that, conditional on its impact, the quantity of output has no or even a negative effect on each of a number of proxies for reputation, and very little evidence that a scholar's most influential work provides any extra enhancement of reputation. Quality ranking matters more than absolute quality. Data on mobility and salaries show, on the contrary, substantial positive effects of quantity, independent of quality. We test various explanations for the differences between the determinants of reputation and salary.mobility, quality/quantity trade-off, salary determination
Will This Paper Increase Your h-index? Scientific Impact Prediction
Scientific impact plays a central role in the evaluation of the output of
scholars, departments, and institutions. A widely used measure of scientific
impact is citations, with a growing body of literature focused on predicting
the number of citations obtained by any given publication. The effectiveness of
such predictions, however, is fundamentally limited by the power-law
distribution of citations, whereby publications with few citations are
extremely common and publications with many citations are relatively rare.
Given this limitation, in this work we instead address a related question asked
by many academic researchers in the course of writing a paper, namely: "Will
this paper increase my h-index?" Using a real academic dataset with over 1.7
million authors, 2 million papers, and 8 million citation relationships from
the premier online academic service ArnetMiner, we formalize a novel scientific
impact prediction problem to examine several factors that can drive a paper to
increase the primary author's h-index. We find that the researcher's authority
on the publication topic and the venue in which the paper is published are
crucial factors to the increase of the primary author's h-index, while the
topic popularity and the co-authors' h-indices are of surprisingly little
relevance. By leveraging relevant factors, we find a greater than 87.5%
potential predictability for whether a paper will contribute to an author's
h-index within five years. As a further experiment, we generate a
self-prediction for this paper, estimating that there is a 76% probability that
it will contribute to the h-index of the co-author with the highest current
h-index in five years. We conclude that our findings on the quantification of
scientific impact can help researchers to expand their influence and more
effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data
Mining (WSDM'15
The Hirsch spectrum: a novel tool for analysing scientific journals
This paper introduces the Hirsch spectrum (h-spectrum) for analyzing the academic reputation of a scientific journal. h-Spectrum is a novel tool based on the Hirsch (h) index. It is easy to construct: considering a specific journal in a specific interval of time, h-spectrum is defined as the distribution representing the h-indexes associated to the authors of the journal articles. This tool allows defining a reference profile of the typical author of a journal, compare different journals within the same scientific field, and provide a rough indication of prestige/reputation of a journal in the scientific community. h-Spectrum can be associated to every journal. Ten specific journals in the Quality Engineering/Quality Management field are analyzed so as to preliminarily investigate the h-spectrum characteristic
A Survey of Quality Engineering-ManagementJournals by Bibliometric Indicators
This paper analyses some of the most popular scientific journals in the Quality field from the point of view of three bibliometric indicators: the Hirsch (h) index for journals, the total number of citations and the h-spectrum. In particular, h-spectrum is a novel tool based on h, making it possible to (i) identify a reference profile of the typical authors of a journal; (ii) compare different journals; and (iii) provide a rough indication of their âbibliometric positioning' in the scientific community. Results of this analysis can be helpful for guiding potential authors and members of the scientific community in the Quality Engineering/Management area. A large amount of empirical data are presented and discusse
Utilising content marketing metrics and social networks for academic visibility
There are numerous assumptions on research evaluation in terms of quality and relevance of academic contributions. Researchers are becoming increasingly acquainted with bibliometric indicators, including; citation analysis, impact factor, h-index, webometrics and academic social networking sites. In this light, this chapter presents a review of these concepts as it considers relevant theoretical underpinnings that are related to the content marketing of scholars. Therefore, this contribution critically evaluates previous papers that revolve on the subject of academic reputation as it deliberates on the individual researchersâ personal branding. It also explains how metrics are currently being used to rank the academic standing of journals as well as higher educational institutions. In a nutshell, this chapter implies that the scholarly impact depends on a number of factors including accessibility of publications, peer review of academic work as well as social networking among scholars.peer-reviewe
The Tale of Two research Communities: The Diffusion of Research on Productive Efficiency
The field of theoretical and applied efficiency analysis is pursued both by economists and people from operational research and management science. Each group tends to cite a different paper as the seminal one. Recent availability of extensive electronically accessible databases of journal articles makes studies of the diffusion of papers through citations possible. Research strands inspired by the seminal paper within economics are identified and followed by citation analysis during the 20 year period before the operations research paper was published. The first decade of the operations research paper is studied in a similar way and emerging differences in diffusion patterns are pointed out. Main factors influencing citations apart from the quality of the research contribution are reputation of journal, reputation of author, number of close followers; colleagues, âcadres of protĂ©gĂ©sâ, Ph.D. students, and extent of network (âinvisible collegeâ). Such factors are revealed by the citing papers. In spite of increasing cross contacts between economics and operations research the last decades co-citation analysis reveals a relative constant tendency to stick to âown campâ references.Farrell efficiency measures, data envelopment analysis, DEA, bibliometry
A new economic journalsâ ranking that takes into account the number of pages and co-authors
In this article, I examine whether the academics reward policy must correlate positively with the number of published articles per co-author, the number of pages and journal reputation. This is accomplished by estimating a non-linear model with a panel data from 168 economics journals covered in the ISI-Web of Knowledge database (58825 articles). The data reinforces the conjecture that published article value is slightly increasing with the number of co-authors and is proportional to the number of pages. The data also suggests that there are 4 distinct groups related to journal quality that I name A, B+, B and Bâ.Co-authorship, Value of articles, Assessment of output
Learning Reputation in an Authorship Network
The problem of searching for experts in a given academic field is hugely
important in both industry and academia. We study exactly this issue with
respect to a database of authors and their publications. The idea is to use
Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform
topic modelling in order to find authors who have worked in a query field. We
then construct a coauthorship graph and motivate the use of influence
maximisation and a variety of graph centrality measures to obtain a ranked list
of experts. The ranked lists are further improved using a Markov Chain-based
rank aggregation approach. The complete method is readily scalable to large
datasets. To demonstrate the efficacy of the approach we report on an extensive
set of computational simulations using the Arnetminer dataset. An improvement
in mean average precision is demonstrated over the baseline case of simply
using the order of authors found by the topic models
Bibliometric Indicators of Young Authors in Astrophysics: Can Later Stars be Predicted?
We test 16 bibliometric indicators with respect to their validity at the
level of the individual researcher by estimating their power to predict later
successful researchers. We compare the indicators of a sample of astrophysics
researchers who later co-authored highly cited papers before their first
landmark paper with the distributions of these indicators over a random control
group of young authors in astronomy and astrophysics. We find that field and
citation-window normalisation substantially improves the predicting power of
citation indicators. The two indicators of total influence based on citation
numbers normalised with expected citation numbers are the only indicators which
show differences between later stars and random authors significant on a 1%
level. Indicators of paper output are not very useful to predict later stars.
The famous -index makes no difference at all between later stars and the
random control group.Comment: 14 pages, 10 figure
Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998â2018)
Opinion mining and sentiment analysis has become ubiquitous in our society, with
applications in online searching, computer vision, image understanding, artificial intelligence and
marketing communications (MarCom). Within this context, opinion mining and sentiment analysis
in marketing communications (OMSAMC) has a strong role in the development of the field by
allowing us to understand whether people are satisfied or dissatisfied with our service or product
in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To
the best of our knowledge, there is no science mapping analysis covering the research about opinion
mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science
mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work
during the last two decades in this interdisciplinary area and to show trends that could be the basis
for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer
and InCites based on results from Web of Science (WoS). The results of this analysis show the
evolution of the field, by highlighting the most notable authors, institutions, keywords,
publications, countries, categories and journals.The research was funded by Programa Operativo FEDER AndalucĂa 2014â2020, grant number âLa
reputaciĂłn de las organizaciones en una sociedad digital. ElaboraciĂłn de una Plataforma Inteligente para la
LocalizaciĂłn, IdentificaciĂłn y ClasificaciĂłn de Influenciadores en los Medios Sociales Digitales (UMA18â
FEDERJAâ148)â and The APC was funded by the same research gran
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