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The first conclusion that can be reached by this When the ranking of journals is compared with study is the difficulty of adequately identifying and that offered by Scopus and WOS, it is confirmed that locating the journals produced worldwide in the scienGSM offers greater indicators almost double and, tific field of communication. None of the databases most relevantly, a high correlation, something already used here are capable of exhaustively monitoring all of the existing journals, for which reason it is necessary to use all three databases together.

Despite the problematic technique implied by this lack of bibliographic control, it is certain that a contributory factor is the multidisciplinary nature of Communication itself, having boundaries so vague as to prevent a clear delimitation 1The table of comparison can be consulted at of the field covered. Communication receives a subshttp: Consequently, it can be affirmed that GSM measures journals in a very similar way to the classic journal evaluation systems WOS and Scopus for which, broadly speaking and for ranking only purposes, it is an equally reliable and valid alternative for measuring the impact of journals.

In short, this paper supplies the h index impact of communication journals. Although this figure represents approximately less than half Journal of Informetrics, 3 4 , Three Options for Citation Tracking: Google Scholar, Scopus and Web of Science. Biomedical Digital Libraries, 3 7. Scientometrics, 74 2 , — Scientometrics 82 3 , — PloS one, 4 6 , e A Hirsch-type Index for Journals. The Sciencist, 19 22 , EC3 Working Paper, 3. EC3 Working Papers, 2. Asian Journal of Communication, 19 2 , Communication Theory as a Field. Communication Theory, 9 2 , Annual Convention of the National Comunication Associations.

Google Scholar Metrics revisado: Ahora empieza a ir en serio. EC3 Working Papers, 8. Cybermetrics, 16 1 , paper 4. Una alternativa internacional, gratuita y de libre acceso para medir el impacto de las revistas de Arte, Humanidades y Ciencias Sociales. EC3 Working Papers, , 5. Annual Review of Information Science and Technology, 44 1 , Journal of Information Science, 21 3 , A Google Scholar H-index for Journals: Ranking Disciplinary Journals with the Google Scholar h-index: Journal of Social Work Education, 47 3 , Human Communication Research, 22 4 , Google Scholar Metrics for Publications: Online Information Review, 36 4 , The Journal of the American Medical Association, 10 , Journal of Communication, 55 1 , Communication Education, 59 1 , Impact of Data Sources on Citation.

Web of Science versus Scopus and Google Scholar. Scientometrics, 81 1 , Citation Networks of Communication Journals, — Human Communication Research, 15 2 , Citation Patterns of Core Communication Jour-. An Assessment of the Developmental Status of Communication. Measuring the Reputation and Productivity of Communication Programs. Communication Education, 57 3 , Ranking Forestry Journals Using the H-index. Journal of Informetrics, 2 4 , This concept raises from the development of new indicators based on Web 2. The basic assumption is that variables such as mentions in blogs, number of twits or of researchers bookmarking a research paper for instance, may be legitimate indicators for measuring the use and impact of scientific publications.

In this sense, these indicators are currently the focus of the bibliometric community and are being discussed and debated. We describe the main platforms and indicators and we analyze as a sample the Spanish research output in Communication Studies. Comparing traditional indicators such as citations with these new indicators. We conclude pointing out the main shortcomings these metrics present and the role they may play when measuring the research impact through 2.

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Introduction Altmetrics is a very new term, and can be defined as the creation and study of new indicators for the analysis of academic activity based on Web 2. The underlying premise is that, for example, mentions in blogs, number of re-tweets or saves of articles in reference management systems, may be a valid measure of the use of scientific publications.

However, measuring the visibility of science on the Internet is not a new phenomenon. This was derived from the application of bibliometric methods to online sites, and encompasses various disciplines including communication. Despite the web playing an increasingly important role in social and economic relations, this discipline has not been able to overcome certain limitations inherent in the methodologies, methods and information sources used.

However, it has contributed a complementary perspective to the traditional analysis of citations by means of the study of links, mailing list communications or analysis of the structure of the academic web. Shortly afterwards, the consolidation of scientific communication by journals and electronic media such as repositories opened the door to new indicators. With a view to measuring scientific impact, these indicators offer complementary information. Without doubt, the idea that traditional bibliometric measures and the sources on which they base their calculations are insufficient permeates throughout the scientific community.

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There is a clear symbiotic relationship between web based and bibliometric methods. It is in this context, with the arrival of Web 2. Although, in a wider sense, any unconventional measure for the evaluation of science can be considered an alternative indicator, sensu stricto it would be more accurate to speak of indicators derived from 2.

Additionally, altmetrics offer a new perspective, considering the almost real time information provided on research impact. This monitoring, in the form of revision by peer collectives or peer revision following publication Mandavilli, , is undoubtedly an element that introduces new forms of scrutiny by the scientific community. Taking into account the impact of Web 2.

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Firstly, an explanation is given of the main platforms and indicators, followed by the comparative evaluation of a selection of communication papers showing the number of citations received and their 2. Next, a review of the principal empirical studies is undertaken, centering on the correlations between bibliometric and alternative indicators. To conclude, the main limitations of altmetrics are highlighted alongside a reflective consideration of the role altmetrics may play when it comes to understanding the impact of research in Web 2.

For example, the number of times a study has been marked as favourite bookmarking or the number of times it has been added to a bibliographic collection. On the other hand, some authors such as Taraborelli , note that these indicators represent a form of quick review, by reflecting the degree to which papers are accepted by the scientific community.

Of these, Mendeley currently arouses the most interest. These measurements are quantitative approximations of the measure of interest aroused within the scientific community, and also amongst a general public, which transcend or compliment the impact of traditional citation indexes.

As can be seen in table 1, there exists a large number of indicators of distinct nature, origin and degree of normalisation. This means that the first difficulty faced when compiling information for a specific publication, and the subsequent altmetric calculation, is the high cost in time and effort.

To solve this problem, a series of tools have emerged to help monitor impact. Some of these tools are altmetric. For scientific papers, statistics are normally presented from Facebook Clicks, Shares, Likes or Comments ,. In their favour, it has to be said that these tools enable the easy recuperation of statistics of collections of papers. However, they are limited by the presentation of contradictory results and only partially recover the statistics. This sample has been compared with a random control group of another 30 papers, comprised of uncited articles from the same journals and years.

In this way, the objective is to verify if a connection exists between the most cited articles and those that show superior data from alternative indicators. The following indicators were calculated for each article: The high occurrence of zeros among the most cited articles can be confirmed, in particular with regard to the indicators of Citeulike. This demonstrates one of the limitations of these statistics, as does the scant representation of some of these tools for reflecting scientific activity.

The frequently cited articles were tweeted on more occasions than studies from the control sample table 3. According to the first source Impact Story , the cited articles were tweeted on average once more than the control sample, which did not receive any tweets. These figures increase to 2. Although, due to the large number of papers not tweeted, the median in all cases is zero. Turning to Citeulike, the social bookmarking tool for scientists, the articles most cited between and were saved an average of 1.

However, the most representative data is that from Mendeley, where the most cited studies have been saved by an average of That is, the most cited papers are also saved more times by academics than uncited papers from the same. A Standard deviation median. Web of Science; IS: With regard to the correlaarticles, depending on the source consulted, present tion between citation and Twitter, Eysenbanch indicators different to zero. The highest correlations between 4.

The corresic bibliometric indicators and the new metrics. These lation with Mendeley reaches 0. Clearly, in the sample of 60 Mendeley and citations in Scopus rises to 0. T h e s e results are in accordance with those obtained in other scientific papers table 4. Therefore, in scientific literature to date, the correlation between any of the altmetrics and the number of citations remains to be convincingly demonstrated.

However, evidence does exist of a certain association between highly cited or frequently downloaded and highly tweeted articles. In the present case the most cited sample table 3 also had higher rates of activity in social networks. The results presented in table 4 suggest that altmetrics measure a dimension of scientific impact that is still to be determined.

It seems apparent that altmetrics capture a different dimension, which could be entirely complementary to citation, given that the different platforms have audiences more diverse than the merely academic. If, for example, the phenomenon is observed from the other perspective, that of papers with greater altmetric impact, the studies most widely diffused across social networks in were not always related to strictly scientific interests, but to cross curricular subjects that better reflected the interests of the general public. For example, some of the scientific articles arousing the greatest interest in social networks in were related to very topical issues such as the Fukushima nuclear accident; cross curricular subjects, such as the effect of coffee consumption on health; or interests closely linked to the profile of a social network user, such as an analysis of classic Nintendo games Noorden, Therefore, it is not strange that altmetrics are starting to equate with the social impact of research.

By way of conclusion: These new indicators should be welcomed as being complementary to traditional metrics. However, due to being very new, and only recently applied in scientific contexts, the use of altmetrics still has certain limitations. This situation is clearly shown by the evanescent nature of its sources; whereas citation indexes such as Web of Science are stable and have trajectories of decades, the same cannot be said of the 2.

In general, platforms which archive papers, and ultimately generate indicators, usually have very exiguous life cycles and can disappear, as happened with the recent disappearance of Connotea in March This means that it is currently difficult to choose a reference tool which guarantees medium term continuity. Many uncertainties still exist as to the reproducibility and final significance of results, especially concerning the scientific relevance of the same. This in turn makes it difficult for these tools to be incorporated into the list of evaluative tools. This causes the compilation of direct mentions, and not indirect article reviews, to be a laborious matter.

Finally, it has to be mentioned that the empirical study undertaken has also enabled confirmation of the scant concordance of ImpactStory or Almetric. Not only is compilation difficult, but also, in most instances, data gathered from many platforms produces very low numbers. Added to this has to be the global difficulty faced by these tools in making data from some of the 2. These indicators should clearly be used for measuring the social impact of science and, above all, for measuring the impact or immediate visibility of publications, an impossibility for citation.

This facet complements the classic indicators and even expert reviews, which altmetrics should not aspire to substitute, a situation and a function noted by most scientists Nature Materials, Additionally, an identifiable role can be played in fields were bibliometrics is most lacking, as may be the case in humanities Sula, It can be stated that new forms of scientific communication require new forms of measurement.

For the moment, the only definite conclusion seems to be that altmetrics is here to stay, to enrich the possibilities and dimensions of impact analysis, in all fields of scientific research, and to illuminate from a new perspective the relationship between science and society. Learned Publishing, 26 1 , Las nuevas alfabetizaciones ante los cambios culturales de la Web 2.

Anuario ThinkEPI, 6, The Journal of Neuroscience, 28 45 , Can Tweets Predict Citations? Journal of Medical Internet Reseach, 13 4 , PloS one, 7 12 , e Journal of Informetrics, 4 3 , Chronicle of Higher Education http: Journal of Informetrics, 5 3 , Scientometrics, 91 2 , Nature Materials, 11, Article-level Metrics and the Evolution of Scientific Impact. PLoS Biology, 7 11 , e Scientific citations in Wikipedia.

First Monday, 12 http: Nature News Blosg http: First Monday, 15 Altmetrics in the Wild: A Case Study in Using Altmetrics. Show me the Data. Journal of Cell Biology, 6 , Comparison of Citation and Usage Indicators: The Case of Oncology Journals. Scientometrics, 82 3 , British Medical Journal, , Article Downloads, Twitter Mentions, and Citations. PloS one, 7 11 , e Visualizing Social Connections in the Humanities: Social Software and Distributed Scientific Evaluation.

Annual Review of Information Science and Technology, 39 1 , ABSTRACT At a time when academic activity in the area of communication is principally assessed by the impact of scientific journals, the scientific media and the scientific productivity of researchers, the question arises as to whether social factors condition scientific activity as much as these objective elements.

This investigation analyzes the influence of scientific productivity and social activity in the area of communication. We identify a social network of researchers from a compilation of doctoral theses in communication and calculate the scientific production of of the most active researchers who sit on doctoral committees. Social network analysis is then used to study the relations that are formed on these doctoral thesis committees.

The results suggest that social factors, rather than individual scientific productivity, positively influence such a key academic and scientific activity as the award of doctoral degrees. Our conclusions point to a disconnection between scientific productivity and the international scope of researchers and their role in the social network. Nevertheless, the consequences of this situation are tempered by the non-hierarchical structure of relations between communication scientists. Introduction The development and the future of scientific activity have generally been treated as endogenous aspects linked to the evolution of research, significant scientific discoveries and the process of transferring scientific knowledge and know-how, etc.

However, for many decades, a strong social element has clearly been identified in scientific activity that can determine its creation, diffusion and demarcation to an extraordinary extent Kuhn, ; Merton, Many scientific communities, to a greater or lesser extent, have geographical boundaries that depend on their scope of knowledge, while academic traditions, linguistic environments and the physical structures of scientific activity more often than not generate its national geographical environment. It is therefore of interest to know the particularities of the scientific communities in each country or region.

In this context, it appears pertinent to look into the social aspects of the Spanish scientific community linked to the field of communication. This change is leading to a slow increase in the specific weight attached to research in the promotion of university teaching staff. At the same time, communication represents a fertile territory, as in other social disciplines with high levels of interdisciplinary studies, in which social aspects are given a prominent place in scientific activity. In addition, it appears especially relevant to link social activity in this field to aspects that are related to scientific communication, as the current trend is to assess scientists and academics in their discipline in accordance with.

However, it would be worth asking whether the weight of such apparently objective measures of scientific prominence publications and citations is the criterion shaping the structure of Spanish communication academia and whether the baseline of social relations between scientists plays a defining role in their scientific activity. In response to that question, we will study how both scientific productivity and social activity influence a key academic decision in the scientific community: Social network analysis was selected as a referential framework in which to conduct an acceptable analysis of social ties between scientists arising from the academic act of the reading of a doctoral thesis Scott, The academic and scientific community in the field of communication The analysis of social factors in scientific production has a long tradition and has generated a particular field of knowledge: The influence of social structures on scientific production may be conceptualised as invisible colleges.

De Solla Price pointed to the existence of groups of scientists that were basically constituted by a contact and by informal communication that generated a stable social structure highlighting the role of the elite within it. Crane ; used an thorships in Spanish communication journals with the incipient network analysis to highlight the appearance highest impact.

This author suggests the need for more of emergent social structures in the scientific field in in-depth studies for an understanding of how these the form of invisible colleges or social circles. To do networks are formed and how they function. This structure with fuzzy limits. Empirical studies focusing on the sible college as the organizational structure of a set of groups that have been identified would be of interest. This change is leading field. Moody used the relation of co-authorship to to a slow increase in the specific weight attached to describe the collaborative networks in social science and research in the promotion of university teaching staff.

At the constructed various models to same time, communication represents a fertile territory, as in test how collaboration affects scientific practice appearance other social disciplines with high levels of interdisciplinary of small relatively isolated groups, exchanges between studies, in which social aspects are given a prominent groups with different interests, and networks dominated by place in scientific activity.

The first two possibilities were also explored by Crane Repiso, Torres, and Delgado b analytie of membership between its members and that will sed social networks in communication on the basis of generate a series of formal and informal contacts betthe members of the doctoral thesis committees. Various researchers have brought up the existence The reading of a doctoral thesis represents an of these networks in communication. It is therefore very important that tween professors belonging to the different Spanish each thesis should be evaluated by qualified researuniversities based on a bibliometric study of co-auchers.

The director of the doctoral thesis and the departmental members intervene in a decisive way in the choice of the committee members through informal processes, which are therefore based on considerations that go beyond the purely scientific. These choices should be based on criteria that should be objective, arising from the research capabilities of the members that sit on the doctoral committees. The scientific productivity of academics is a measure of the success of their scientific activity, marking the road.

In an empirical way and using social network analysis, Sierra demonstrated, on the basis of the composition of CSIC thesis committees, that the choice of committee members did not follow random criteria, but that there is a social grounding for those decisions. Likewise, Casanueva and Larrinaga presented evidence that social factors and, in particular, the previous contact between other members of the network significantly influenced the choice of doctoThe scientific productivity of academics is a measure of the ral committee members and their chairpersons in the discisuccess of their scientific activity, marking the road towards pline of accounting and finance.

The following hypothesis professional progress. Therefore, the professional developmay therefore be formulated: Therefore, the professional development of researchers and, consequently, their selection by the academic community to conduct research-related activities will be conditioned by what they are objectively able to contribute. The selection of doctoral committee members in the field of communication will be positively influenced by their scientific productivity, measured by their publications and the number of citations received.

Furthermore, a complementary hypothesis may be developed that links social factors to relevant decisions of scientific activity in communication. It appears logical to think that social structures that take shape in the network of researchers and academics in an area of knowledge might determine or condition the evaluation of a first rate piece of research and the accreditation within the scientific community of the investigative worth of the doctorand. The network in the field of communication based on doctoral committees The network constituted by researchers and academics from the field of communication who have participated in doctoral committees from up until is selected as the area of study, in order to test the two hypotheses on the influence of scientific productivity and social activity in scientific decisions.

The Teseo database was used to demarcate the area of study, which provides different information on doctoral theses read in Spain. The definition of the theses within the area is complicated, insofar as there are no suitable descriptors that mark out clear frontiers, without overlaps in the area of communication. Therefore, our strategy involved the identification of all theses produced in departments of audiovisual communication, marketing and journalism from all Spanish universities.

Almost different doctors had a role in those theses as directors and members of the doctoral committees, as researchers in the same or in other similar disciplines in Spain or as foreign doctors. Many of these actors had no relevant role in the network. A relational criterion was therefore chosen, in line with Laumann and others , when defining the network, in order to conduct a more suitable empirical analysis that would respond to the purpose of this investigation, in such a way that only those doctors who sat on eight committees or more were analysed.

This meant a more manageable and sufficiently broad network in terms of its analysis that would limit itself to the most active doctors on the doctoral committees. Data obtained on these researchers in the field of communication refer to their affiliations and to their scientific productivity. The number of publications and the number of citations from those same publications were used for the calculation of scientific production.

The information contained in the most standardized international databases SSCI and Scopus produced no search results that clearly differentiated between the members chosen from the network in terms of their scientific production. For example, only This finding is consistent with earlier studies that described the limited internationalization of publications from communication academics in Spain Masip, So, we referred to Google Scholar to obtain the most important data on the scientific production of the actors.

Data referring to articles in journals, books, and chapters of books were all considered. Manual inspection the data gathered in this way and its registration was done, as this tool is not very discriminatory with regard to names and document types. Analysis of social networks The study of the influence of social relations in academic decisions, and more specifically in the selection processes for the committees that will evaluate doctoral theses should pay specific attention to the social relations that they engender and the social structure that arises from them.

An acceptable analysis of social structures should be based on specific data, not on the characteristics, but on the social ties of the individual. Social network analysis attempts to reveal the overall structure of the ties between actors, identifying the existence of general relational patterns that result from the abstraction of individual choices or from the links between the nodes. A network may be defined in a simple way as a set of interrelated nodes. So, the starting point of network analysis is the study of these two basic units: Since its recent origin, social network analysis has been applied to the study of scientific activity Crane, It has undergone notable development over recent years with the availability of massive bibliographies on co-authorships in scientific publications Moody, ; Newman, Variables Different regression models were prepared to test the hypothesis, the variables of which are explained below: As an outcome variable, the dependent variable used the sum of the times that each of the doctors who represent the sample of the most active doctors was chosen to participate in a doctoral committee.

As mentioned earlier, the minimum value of this variable was set at 8. Four basic indicators of scientific productivity were used for their measurement. These included books, book chapters, and publications in scientific journals that have been cited at least once.

They are taken in aggregate, without differentiating between document types. The third variable seeks an overall measurement of publication capacity and of the impact of the published documents measured by the number of citations they have received: The pregraph of the network, it is better to study the indicators paration of the indicator of social activity was more that arise from the analysis of social networks, as gracomplex.

In the first place, a new network was consphic representations offer a very limited scope for tructed, in which the link under consideration was the analysis. Table 1 presents the most relevant indicators joint presence of academics at the reading of a doctoof the complete network of the selection of the doctoral thesis. In other words, each thesis brings together ral committee members in the field of communication committee members, directors, and co-directors of the together with those same indicators referring to the thesis at a single academic act from which other social network that comprises the doctors selected for events often arise.

This mutual contact means that the the empirical analysis. Data on academic networks members of the network get to know each other or from another two areas of the social sciences are their familiarity is deepened. The first of these is made up of the The first row of table 1 shows the size of the netcore of the network and second by its periphery. The size of the network of toral committees in the field of communication.

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Two control variables were under consideration is , while the ties between used. A dichotomic variable was constructed with a value of 1 for academics that occupy a university chair. Graph of the network of researchers. The low density of the complete commuindex for Results make up that network in relation to the number of Graph 1 shows the network of the most active people that can intervene in each event reading of a doctoral committee members. Even though the existhesis.

In fact, the density is twice that of the two previously mentioned areas of knowledge, such that selection in the field of communication is considerably more interconnected than in other areas of the social sciences in Spain. Indegree centralization indicates how the network is concentrated around certain points, but the level for ties relating to selection is very low in the complete network 2.

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It leads one to think that the network is not very centralized and, therefore, not very hierarchical. This is very important, as its suggests that the academic act of reading a thesis is quite open to the participation of many actors and is not focused on a social structure with a dominant central core. Conversely, outdegree centralization is an indicator of the level at which the thesis management process is focused on a few doctors. Similar values are found in the two other areas under analysis. Betweeness centralization presents low values in the four networks presented in table 1, such that only.

This situation is an indicator that the network is well connected and that anyone can access another node in the network along different paths. Once again, it suggests that this structure is far removed from a hierarchical one. Table 2 shows the mean and the standard deviation of the previously explained variables. The most striking point is that average scientific productivity of the most active members of the doctoral committees in the field of communication is quite high, close to 20 publications with at least one citation on average, the same as the impact of the journal, as the average number of citations that they have is This last point should be qualified, as the dispersion is very high.

These data may be explained because there are certain members of the network with numerous citations, basically because their works are standard references in their field. The fact that approximately half of the network members are university chairs and that a third participates or have participated in the management of the most relevant scientific journals in the field is also noteworthy. Table 3 presents three regression models. The standardized coefficients of the variables and their level of meaning appears in the same table. Model 1 is the control model.

It includes the control variables University Chair and Editorial Board. The results show a positive and significative relation although at a. Model 2 is intended to test Hypothesis 1.

The four variables that measure scientific productivity now intervene as independent variables. Almost no increase in the explained variance was observed as a result of the inclusion of the new variables in the model. Once again, a positive and significative relation was shown in Model 2 between the condition of university chair and the dependent variable, whereas the relations with the four independent variables that measure scientific productivity Publications, Citations, h-Index and Internationalization are not significative.

No support is therefore forthcoming for Hypothesis 1. The first thing that may be seen is the important increase of R2 that rises to a value of 0.

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Hypothesis 2, which states that the selection of doctoral committee members in the field of communication is positively associated with the social activity of the academics, is therefore confirmed. Amazon Music Stream millions of songs. Amazon Advertising Find, attract, and engage customers. Amazon Drive Cloud storage from Amazon. Alexa Actionable Analytics for the Web. AmazonGlobal Ship Orders Internationally. Amazon Inspire Digital Educational Resources. Amazon Rapids Fun stories for kids on the go.

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