Regression for citation data: An evaluation of different methods

Online Visibility and Scientific Relevance of Strabismus Research: Bibliometric Analysis

BACKGROUND

Quality and accuracy of online scientific data are crucial, given that the internet and social media serve nowadays as primary sources of medical knowledge.

OBJECTIVE

This study aims to analyze the relationship between scientific relevance and online visibility of strabismus research to answer the following questions: (1) Are the most popular strabismus papers scientifically relevant? (2) Are the most high-impact strabismus studies shared enough online?

METHODS

The Altmetric Attention Score (AAS) was used as a proxy for online visibility, whereas citations and the journal's impact factor (IF) served as a metric for scientific relevance. Using "strabismus" as a keyword, 100 papers with the highest AAS and 100 papers with the highest number of citations were identified. Statistical analyses, including the Spearman rank test, linear regression, and factor analysis, were performed to assess the relationship between AAS, citations, a journal's IF, and mentions across 18 individual Web 2.0 platforms.

RESULTS

A weak, positive, statistically significant correlation was observed between normalized AAS and normalized citations (P<.001; r=0.27) for papers with high visibility. Only Twitter mentions and Mendeley readers correlated significantly with normalized citations (P=.02 and P<.001, respectively) and IF (P=.04 and P=.009, respectively), with Twitter being the strongest significant predictor of citation numbers (r=0.53). For high-impact papers, no correlation was found between normalized citations and normalized AAS (P=.12) or the IF of the journal (P=.55).

CONCLUSIONS

While clinical relevance influences online attention, most high-impact research related to strabismus is not sufficiently shared on the web. Therefore, researchers should make a greater effort to share high-impact papers related to strabismus on online media platforms to improve accessibility and quality of evidence-based knowledge for patients.

Inverted U-Shaped relationship between team size and citation impact: Mediating role of responsibility diffusion

Despite the increasing prominence of research collaboration, a growing number of studies have confirmed that increasing team size can have limited performance benefits. However, the mechanism of this phenomenon has yet to be established. This study, therefore, quantified responsibility diffusion based on author contribution information and explored its mediating role in the relationship between collaboration size and citation impact (citation count in a four-year window). The results show the following: (1) An inverted U-shaped relationship exists between team size and citation count. (2) Responsibility diffusion plays a partial mediating role between team size and citation count. (3) As team size increases, the degree of responsibility diffusion increases. Lastly, (4) responsibility diffusion has an inverted U-shaped curvilinear relationship with citation count (e.g., a moderate degree of responsibility diffusion has the highest impact). These findings offer a new understanding of the mechanism by which collaboration size influences research performance. This study also has practical implications for solving research collaboration dilemmas based on a group-cognition perspective.

Characteristics, level of evidence, and impact of clinical studies on peri-implantitis: 2017 to 2021

BACKGROUND

The level of evidence (LOE) is an important tool in current evidence-based practice and clinical research. However, in clinical studies on peri-implantitis, the present status of the LOE and its association with research impact remains to be determined. The present study aimed to gather the characteristics and LOE of clinical studies on peri-implantitis conducted from 2017 to 2021 and assess the association of the LOE with social and scientific impact.

METHODS

The PubMed database was searched to retrieve clinical studies that evaluated peri-implantitis-related healthcare interventions and were published between 2017 and 2021. A 4-level modified Oxford 2011 LOE tool was used to determine the LOE of these studies. Citation count and Altmetric Attention Scores (AAS) were derived from the Web of Science and Altmetric Explorer, respectively. Multivariate generalized estimation equation (GEE) analysis was conducted to explore relationships between the LOE and citation count, and between the LOE and AAS; the publication year was considered the grouping factor for adjusting for potential clustering effects.

RESULTS

Two hundred and thirty-five studies were considered eligible. The percentages by level from Level-1 to Level-4 were 9.8%, 35.7%, 28.9%, and 25.5%. Multivariable GEE analyses revealed that studies with higher LOE ratings had significantly greater citation counts (p = 0.008). However, no significant association (p = 0.872) was observed between the LOE and AAS.

CONCLUSIONS

From 2017 to 2021, more than 40% of studies published on peri-implantitis each year had high-LOE ratings. High-LOE studies showed a tendency to have greater scientific impact.

Why are medical research articles tweeted? The news value perspective

Counts of tweets mentioning research articles are potentially useful as social impact altmetric indicators, especially for health-related topics. One way to help understand what tweet counts indicate is to find factors that associate with the number of tweets received by articles. Using news value theory, this study examined six characteristics of research papers that may cause some articles to be more tweeted than others. For this, we manually coded 300 medical journal articles about COVID-19. A statistical analysis showed that all six factors that make articles more newsworthy according to news value theory (importance, controversy, elite nations, elite persons, scale, news prominence) associated with higher tweet counts. Since these factors are hypothesised to be general human news selection criteria, the results give new evidence that tweet counts may be indicators of general interest to members of society rather than measures of societal impact. This study also provides a new understanding of the strong positive relationship between news mentions and tweet counts for articles. Instead of news coverage attracting tweets or the other way round (journalists noticing highly tweeted articles and writing about them), the results are consistent with newsworthy characteristics of articles attracting both tweets and news mentions.

Assessing books’ academic impacts via integrated computation of multi-level citation information

Purpose

Citations have been used as a common basis to measure the academic accomplishments of scientific books. However, traditional citation analysis ignored content mining and without consideration of citation equivalence, which may lead to the decline of evaluation reliability. Hence, this paper aims to integrate multi-level citation information to conduct multi-dimensional analysis.

Design/methodology/approach

In this paper, books’ academic impacts were measured by integrating multi-level citation resources, including books’ citation frequencies and citation-related contents. Specifically, firstly, books’ citation frequencies were counted as the frequency-level metric. Secondly, content-level metrics were detected from multi-dimensional citation contents based on finer-grained mining, including topic extraction on the metadata and citation classification on the citation contexts. Finally, differential metric weighting methods were compared with integrate the multi-level metrics and computing books’ academic impacts.

Findings

The experimental results indicate that the integration of multiple citation resources is necessary, as it can significantly improve the comprehensiveness of the evaluation results. Meanwhile, compared with the type differences of books, disciplinary differences need more attention when evaluating the academic impacts of books.

Originality/value

Academic impact assessment of books via integrating multi-level citation information can provide more detailed evaluation information and cover shortcomings of methods based on single citation data. Moreover, the method proposed in this paper is publication independent, which can be used to measure other publications besides books.

Scientific laws of research funding to support citations and diffusion of knowledge in life science

One of the main problems in scientometrics is to explore the factors that affect the growth of citations in publications to identify best practices of research policy to increase the diffusion of scientific research and knowledge in science and society. The principal purpose of this study is to analyze how research funding affects the citation-based performance of scientific output in vital research fields of life science, which is a critical province (area of knowledge) in science to improve the wellbeing of people. This study uses data from the Scopus database in 2015 (to assess the impact on citations in 2021, after more than 5 years) concerning different disciplines of life science, given by “agricultural and biological sciences”, “biochemistry, genetics, and molecular biology”, “Immunology and microbiology”, “neuroscience” and “pharmacology, toxicology and pharmaceutics”. Results demonstrate that although journals publish un-funded articles more than funded publications in all disciplines of life science, the fraction of total citations in funded papers is higher than the share in the total number of publications. In short, funded documents receive more citations than un-funded papers in all research fields of life science under study. Findings also support that citations of total (funded + un-funded), funded, and un-funded published papers have a power-law distribution in all five research fields of life science. Original results here reveal a general property in scientific development: funded research has a higher scaling potential than un-funded publications. Critical implications of research policy, systematized in a decision-making matrix, suggest that R&D investments in “Neuroscience” can generate a positive impact of scientific results in science and society-in terms of citations-higher than other research fields in medicine. Overall, then, results here can explain some characteristics driving scientific change and help policymakers and scholars to allocate resources towards research fields that facilitate the development and diffusion of scientific research and knowledge in life science for positive societal impact.

Predicting the impact of American Economic Review articles by author characteristics

Authors who publish in American Economic Review (AER) have career paths confined to a few prestigious institutions, and they mostly have exceptional past publication performance. In this paper, we show that authors who are educated and work in the top ten institutions and have better past publication performance receive more citations for their current AER publications. Authors who have published in the top economic theory journals receive fewer citations even after controlling for the subfield of their AER article. The gender of the authors, years of post-PhD experience, and the location of the affiliated institution do not have any significant effect on the citation performance. An opportunistic editor can exploit the factors that are related to citation performance to substantially improve the citation performance of the journal. Such an opportunistic behavior increases the over-representation of authors with certain characteristics. For example, the opportunistic editor who uses the predicted citation performance of articles to select a quarter of the articles increases the ratio of authors who works at the top ten institutions from 30.8 percent to 52.0 percent.

Peer Review

https://publons.com/publon/10.1162/qss_a_00180

Ranking of Iranian medical universities based on altmetric indices

This study was aimed at evaluating the Iranian medical universities’ rankings and altmetric indices in ResearchGate and Academia.edu. This cross-sectional analytical study was conducted using a scientometric method. Social networking measures were collected in MS Excel from January to February 2017. Data were analysed using SPSS software and the Spearman, chi-square and Kendall rank coefficient tests. Ranking information and altmetric indices of 50 Iranian medical universities were collected and analysed. All of the type-1 medical universities have been presented in the Academia.edu and ResearchGate social networks. A statistically significant relationship ( P < 0.05) has been found between the ranking systems of the universities with altmetric indicators, such as the number of members in both social networks, the number of publications and the total RG score in ResearchGate. In addition, there was a significant correlation between the ranking of medical universities based on the H-index of science metrics with their presence in the ResearchGate social network ( P < 0.05). The results of this study reveal a relationship between the universities’ ranking systems and altmetric indicators. These findings emphasise the necessity of increasing the presence of faculty members in social network activities in disseminating and sharing knowledge.

Article promotion on Twitter and Facebook: A case study of Cell journal

Social media has become an increasingly important channel of scholarly communication, especially for promoting the latest research outputs, so its role in facilitating access to academic texts is worth exploring. Based on 324 posts containing scholarly articles shared by journal Cell on Twitter and Facebook, this study compared the user engagement performance of articles posted on both platforms and examined the effect of such social media promotion and user engagement on article visiting. The user engagement performance of the articles was measured by retweets, shares, reactions, and likes, while click data tracked through bitly.com were used to indicate article visits. Statistical analysis, correlation analysis, and regression analysis were applied to explore and understand these data. For Cell, Facebook posts have a more significant influence than similar tweets in terms of volume. The user engagement on Facebook is 2.5~4 times as much as on Twitter. Moreover, the click metric of short links shows that Cell’s posts on Facebook directed twice as many visitors to the papers as posts on Twitter. However, the efficiency of the two platforms is approximate when the difference in the volume of followers is eliminated. The correlation and regression analysis suggested that user engagement positively affects the visiting of Cell’s papers. Both reactions and shares would affect the clicks of the short links to paper text. The results shed light on the implications of sharing scholarly articles on social media platforms for the promotion of article visits.

Information and Scientific Impact of Advanced Therapies in the Age of Mass Media: Altmetrics-Based Analysis of Tissue Engineering

BACKGROUND

Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-stage organs. Its development has taken place since the last decade of the 20th century, entailing a clinical revolution. TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact.

OBJECTIVE

The objective of this study was to evaluate the online dimension of TE documents from 2012 to 2018 using metadata obtained from the Web of Science (WoS) and Altmetric and to develop a prediction equation for the impact of TE documents from altmetric scores.

METHODS

We analyzed 10,112 TE documents through descriptive and statistical methods. First, the TE temporal evolution was exposed for WoS and 15 online platforms (news, blogs, policy, Twitter, patents, peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, video, and Mendeley Readers). The 10 most cited TE original articles were ranked according to the normalized WoS citations and the normalized Altmetric Attention Score. Second, to better comprehend the TE online framework, correlation and factor analyses were performed based on the suitable results previously obtained for the Bartlett sphericity and Kaiser-Meyer-Olkin tests. Finally, the linear regression model was applied to elucidate the relation between academics and online media and to construct a prediction equation for TE from altmetrics data.

RESULTS

TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers, and Altmetric Scores. However, WoS and Altmetric rankings for the most cited documents clearly differ. When compared, the best correlation results were obtained for Mendeley Readers and WoS (ρ=0.71). In addition, the factor analysis identified 6 factors that could explain the previously observed differences between academic institutions and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than 40% of TE WoS citations from Altmetric scores.

CONCLUSIONS

Scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because the focus of TE research importantly differs when the academic institutions and online platforms are compared, basic and clinical research groups, academic institutions, and health politicians should make a coordinated effort toward the design and implementation of adequate strategies for information diffusion and population health education.

A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values

Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just the mean. We focus on two challenges for citation count analysis by quantile regression: discontinuity and substantial mass points at lower counts. A Bayesian hurdle quantile regression model for count data with a substantial mass point at zero was proposed by King and Song (2019). It uses quantile regression for modeling the nonzero data and logistic regression for modeling the probability of zeros versus nonzeros. We show that substantial mass points for low citation counts will almost certainly also affect parameter estimation in the quantile regression part of the model, similar to a mass point at zero. We update the King and Song model by shifting the hurdle point past the main mass points. This model delivers more accurate quantile regression for moderately to highly cited articles, especially at quantiles corresponding to values just beyond the mass points, and enables estimates of the extent to which factors influence the chances that an article will be low cited. To illustrate the potential of this method, it is applied to simulated citation counts and data from Scopus.

The Effect of Replications on Citation Patterns: Evidence From a Large-Scale Reproducibility Project

Replication of existing research is often referred to as one of the cornerstones of modern science. In this study, I tested whether the publication of independent replication attempts affects the citation patterns of the original studies. Investigating 95 replications conducted in the context of the Reproducibility Project: Psychology, I found little evidence for an adjustment of citation patterns in response to the publication of these independent replication attempts. This finding was robust to the choice of replication criterion, various model specifications, and the composition of the contrast group. I further present some suggestive evidence that shifts in the underlying composition of supporting and disputing citations have likely been small. I conclude with a review of the evidence in favor of the remaining explanations and discuss the potential consequences of these findings for the workings of the scientific process.

Determinants of societal and academic recognition: Evidence from randomised controlled trials

Given the increasing importance of recognition in academia and the vital role of randomised controlled trials (RCTs) in medical research and clinical decisions, this study verifies how RCTs’ academic and societal impacts are affected by visibility factors, subjects and methodological validity. This study concentrated on a sample of 446 RCTs indexed in Scopus and evaluated by Cochrane reviewers in terms of their methodological validity. The altmetrics, bibliometric and bibliographical information were extracted from Altmetric.com and Scopus, and the contributing countries’ development ranks were obtained from the United Nations Development report. The linear regression analyses revealed that citations and altmetrics depend on some subjects. They are also affected by publication year and journals’ previous reputation. Citations are also affected by keyword counts and reference counts. Keyword counts and contributing countries’ developmental rank also predict the tweet counts. While none of the methodological validity dimensions were found to predict citations, ‘Incomplete Outcome Data’ and ‘Random Sequence Generation’ significantly, though slightly, affect Mendeley Readership and tweets, respectively. By confirming the dependence of RCTs’ recognition on some methodological validity features and attention-inducing characteristics, the study provides further evidence on the interaction of quality and visibility dynamisms in the recognition network and the complementary role of societal mentions for academic citation.

Is Time-Driven Activity-Based Costing Coming out on Top? A Comparison with Activity-Based Costing in the Health Field

The cost of health is a recurrent topic that has generated much research, as it affects all of society. Both public and private agents need to know the real cost of treatments, services, and products for decision-making. This article aims to compare the use and research impact of two cost systems widely used in health: ABC and TDABC, which is an evolution of ABC. For doing so, a bibliometric review in Scopus and Medline was carried out encompassing the years 2009-2019. The results show a great increase in publications using TDABC, while publications on ABC stabilized. On the other hand, the TDABC articles presented higher research impacts in traditional and alternative metrics. Articles on TDABC are more frequently cited, published in better journals, and more visible in academic social networks. The findings suggest that scholars and practitioners should focus on TDABC rather than ABC for addressing cost in health for its simplicity, projection, and research opportunities.

Technology and Innovation in China: A Patent Citation-based Analysis

With the unique dataset from the State Intellectual Property Office (SIPO) of the People’s Republic of China, this study aims to provide a patent citation-based analysis of technological innovation in China. This study provides an overall description of Chinese invention patent citations, including citations added by both inventors and examiners. Specifically, our work analyses the inventor citations and examiner citations of both international- and domestic-granted patents in SIPO from four different aspects: citations per patent, foreign citation dependence, highly cited patents and current impact index. Our research results indicate the following: (a) the quantity of citations per patent added by foreign applicants was much larger than that of the Chinese applicants in SIPO, and both decreased year after year. (b) Chinese applicants cited many technological references from seven major foreign countries and drew greatly from external technology and science, but Chinese applicants tended to refer more to domestic technology. (c) The overall impact of Chinese patents became stronger, but invention patents in some developed regions had only relatively weak technological impact. (d) The number of high-quality invention patents surged in every province, but high-quality patents remain mainly distributed in four developed areas. Finally, we propose several suggestions from a policy perspective.

Modeling the co-citation dependence on semantic layers of co-cited documents

Purpose

Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.

Design/methodology/approach

The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.

Findings

The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.

Originality/value

It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.

Peer review

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

Chiropractic case reports: a review and bibliometric analysis

Objective

To determine publication trends, gaps, and predictors of citation of chiropractic case reports (CRs).

Methods

A bibliometric review was conducted by searching PubMed, Index to Chiropractic Literature (ICL), and Google Scholar to identify PubMed-indexed CRs, which were screened according to selection criteria. Case reports were categorized by International Classification of Disease (ICD-10) code, patient age, topic describing case management or adverse effects of care, focus being spinal or non-spinal, journal type, integrative authorship, title metrics, and citation metrics. Binary logistic regression was used to identify independent predictors of citations per year and total citations greater than the median values.

Results

The search identified 1176 chiropractic CRs meeting selection criteria. There was an increasing trend of CRs having a case management topic, non-spinal focus, non-chiropractic journal, neuromusculoskeletal-focus, diagnosis of vascular pathology, and a decreasing trend of adverse effect vascular pathology CRs. Independent predictors of greater total citations (or citation rate) included ICD-10 categories of perinatal conditions, infections, “case” in title, case management topic, and physical therapy, integrative, and dental journal type. Predictors of fewer citations included diseases of the blood, neoplasms, other findings not elsewhere classified, a title > 11 words, and multidisciplinary authorship. ICD-10 categories describing non-musculoskeletal diseases and special populations such as pediatrics, pregnancy, and perinatal conditions had few CRs.

Conclusion

Chiropractic CRs are diversifying from spine-related topics. Chiropractors are encouraged to publish objective, structured CRs within defined research gaps. Published CRs can inform the design of future research studies with a higher level of clinical relevance and evidence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12998-021-00374-5.

Gender issues in fundamental physics: A bibliometric analysis

I analyze bibliometric data about fundamental physics worldwide from 1970 to now, extracting quantitative data about gender issues. I do not find significant gender differences in hiring rates, hiring timing, career gaps and slowdowns, abandonment rates, citation, and self-citation patterns. Furthermore, various bibliometric indicators (number of fractionally counted papers, citations, etc.) exhibit a productivity gap at hiring moments, at career level, and without integrating over careers. The gap persists after accounting for confounding factors and manifests as an increasing fraction of male authors going from average to top authors in terms of bibliometric indices, with a quantitative shape that can be fitted by higher male variability.

COVID-19 research in Wikipedia

Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 were created, accumulating over 400 million page views by mid-June 2020. 1 At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia’s content is based on reliable sources, such as scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature that is highly cited, widely shared on social media, and peer-reviewed.

Trends in the level of evidence and impact of clinical studies published in leading oral implantology journals: 2008-2018

OBJECTIVES

To present the characteristics and level of evidence (LOE) of clinical studies published in leading oral implantology journals during 2008-2018 and to explore whether the LOE of a study is associated with its scientific and social impact.

MATERIALS AND METHODS

Clinical studies with direct relevance to the evaluation of healthcare interventions published in 2008, 2013, and 2018 in six oral implantology journals were identified via hand searches. A modified 4-level Oxford 2011 LOE tool was used to assess the LOE of all eligible studies. The citation count and Altmetric Attention Score (AAS) of each study were extracted from Web of Science and Altmetric Explorer, respectively. Thereafter, multivariable generalized estimation equation analyses were used to investigate the association between LOE, citation counts, and AAS, adjusting for potential confounding factors and clustering effects.

RESULTS

A total of 763 clinical studies were included, among which the proportion of level-1, level-2, level-3, and level-4 studies was 2.4%, 30.4%, 40.2%, and 27.0%, respectively. During 2008-2018, the proportion of high LOE studies (level-1 and level-2) increased substantially from 24.6% to 43.1%, although the number of systematic reviews that only include randomized controlled trials has remained limited. According to multivariable analyses, the citation count (p = .002) and AAS (p = .005) of high LOE studies were both significantly greater than those of low LOE studies.

CONCLUSIONS

During the past decade, the proportion of high LOE studies has increased substantially in the field of oral implantology. Clinical studies with higher LOE tend to have greater scientific and social impact.

How do academia and society react to erroneous or deceitful claims? The case of retracted articles’ recognition

Researchers give credit to peer-reviewed, and thus, credible publications through citations. Despite a rigorous reviewing process, certain articles undergo retraction due to disclosure of their ethical or scientific deficiencies. It is, therefore, important to understand how society and academia react to the erroneous or deceitful claims and purge the science of their unreliable results. Applying a matched-pairs research design, this study examined a sample of medicine-related retracted and non-retracted articles matched by their content similarity. The regression analysis revealed similarities in obsolescence trends of the retracted and non-retracted groups. The Generalized Estimating Equations showed that citations are affected by the retraction status, life after retraction, life cycle and the journals’ previous reputation, with the two formers being the strongest in positively predicting the citations. The retracted papers obtain fewer citations either before or after retraction, implying academia’s watchful reaction to the low-quality papers even before official announcement of their fallibility. They exhibit an equal or higher social recognition level regarding Tweets and Blog Mentions, while a lower status regarding Mendeley Readership. This could signify social users’ sensibility regarding scientific quality since they probably publicise the retraction and warn against the retracted items in their tweets or blogs, while avoiding recording them in their Mendeley profiles. Further scrutiny is required to gain insight into the sensibility, if any, about scientific quality. The study’s originality relies on matching the retracted and non-retracted papers with their topics and neutralising variations in their citation potentials. It is also the first study comparing the groups’ social impacts.

The level of evidence, scientific impact and social impact of clinical studies in periodontology: A methodological study

AIMS

To analyse the level of evidence (LOE) of clinical studies in the field of periodontology, and to investigate whether LOE is a predictor of scientific impact and social impact.

MATERIALS AND METHODS

Clinical studies published in five leading periodontal journals during 2015-2019 were identified. The LOE of included studies were assessed with a modified LOE classification system based on Oxford 2009 LOE, Oxford 2011 LOE and GRADE guidelines. Citation counts were harvested from Web of Science and Scopus. Altmetric Attention Scores (AAS) were obtained from Altmetric Explorer. Multivariable generalized estimation equation (GEE) analyses were used to investigate association between LOE and citation count, as well as between LOE and AAS.

RESULTS

Among 768 studies included, the proportion of level-1, level-2, level-3 and level-4 was 10.4%, 44.8%, 13.7% and 31.1%, respectively. In the multivariable GEE analyses, high LOE was a significant predictor of higher average citation count (p = .010) and higher AAS (p < .001).

CONCLUSION

The LOE of clinical studies in the periodontal field is relatively high in general, although it varies significantly in different journals. Studies with high LOE tend to have greater scientific impact and social impact than low LOE studies.

Do proceedings papers in science fields have higher impacts than those in the field of social science and humanities?

Purpose

This study aims to compare the impacts of proceedings papers in the fields of social science and humanities (SSH) and science.

Design/methodology/approach

This study involved not only citations but also altmetric indexes to compare the impacts of proceedings papers among multiple disciplines with 1,779,166 records from Conference Proceedings Citation Index (CPCI) in the Web of Science (WoS) in the period of 2013–2017. The mean value, concentration ratio, Lorenz curves and correlation analysis are utilized into the comparative analysis.

Findings

(1) Proceedings papers in science fields had higher scholarly impacts than those in SSH fields. (2) As for societal impact, clinical, pre-clinical and health still ranked first, whereas physical science and engineering and technologies were transcended by SSH fields, which is different from the scholarly impact of proceedings papers. (3) As for proceedings papers, citations and altmetric indexes have weak or moderate correlations in all six fields, indicating that altmetrics can be supplemented when assessing proceedings papers.

Originality/value

This study is expected to enhance the understanding of proceedings papers and to promote accuracy of evaluation for them by exhibiting the multidisciplinary differences of their scholarly and societal impacts.

Human Resource Disclosures in Corporate Annual Reports of Insurance Companies: A Case of Developing Country

Employees are key stakeholders for companies to maintain their sustainability. Obtaining and disclosing information related to employees can help companies to manage and evaluate the effectiveness of human resources. This paper investigates human resource disclosures in corporate annual reports of 54 insurance companies in Turkey and identifies the determinants of the disclosure for the period of 2007–2017. For this purpose, human resource disclosures with eight subdimensions as employee health and safety, employment of minorities or women, disabled employee, employee training, employee assistance and benefits, employee remuneration, employee profiles and employee morale were obtained from corporate annual reports by content analysis. The data were statistically tested with correlation analysis and a pooled OLS (Ordinary Least Squares) models to determine the effects of return on assets, return on equity, leverage, firm size, number of employees, age of firm, public listing status, foreign ownership, company type on these disclosures. The results indicate that number of employees, foreign ownership and company type have an effect on the extent of human resource disclosure. Employee training is the most disclosed item among human resource disclosures in corporate reports. It is suggested that companies should improve their reporting and disclosure practices related to human resource development.

The citation advantage of linking publications to research data

Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository-rather than being available on request or included as supporting information files-are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.

The relationship between bioRxiv preprints, citations and altmetrics

A potential motivation for scientists to deposit their scientific work as preprints is to enhance its citation or social impact. In this study we assessed the citation and altmetric advantage of bioRxiv, a preprint server for the biological sciences. We retrieved metadata of all bioRxiv preprints deposited between November 2013 and December 2017, and matched them to articles that were subsequently published in peer-reviewed journals. Citation data from Scopus and altmetric data from Altmetric.com were used to compare citation and online sharing behavior of bioRxiv preprints, their related journal articles, and nondeposited articles published in the same journals. We found that bioRxiv-deposited journal articles had sizably higher citation and altmetric counts compared to nondeposited articles. Regression analysis reveals that this advantage is not explained by multiple explanatory variables related to the articles’ publication venues and authorship. Further research will be required to establish whether such an effect is causal in nature. bioRxiv preprints themselves are being directly cited in journal articles, regardless of whether the preprint has subsequently been published in a journal. bioRxiv preprints are also shared widely on Twitter and in blogs, but remain relatively scarce in mainstream media and Wikipedia articles, in comparison to peer-reviewed journal articles.

Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined

In this study, I developed operational versions of Gini’s and Hirsch’s indexes that can be applied to characterize each researcher’s publication history (PH) as heterodox, orthodox, and interdisciplinary. In addition, the new indicators warn against anomalies that potentially arise from tactical or opportunistic citation and publication behaviors by authors and editors, and can be calculated from readily available information. I split the original Hirsch index into nested indexes to isolate networking activity, as well as to distinguish scientific production (number of articles) from scientific productivity (rate of production), and used nested Gini indexes to identify intentional and successful intertopical and interdisciplinary research. I applied the most popular standardizations (i.e., per author and per year), and used simple methodologies (i.e., least-squares linear and cubic fitting, whole-career vs. subperiods, two-dimensional graphs). I provide three representative numerical examples based on an orthodox multidisciplinary PH, a heterodox PH from the social sciences, and an orthodox unidisciplinary PH from the physical sciences. Two additional numerical examples based on PHs from the life and health sciences show that the suggested PH characterization can be applied to different disciplines where different publication and citation practices prevail. Software is provided to help readers explore the use of these indicators.

Order matters: Alphabetizing in-text citations biases citation rates

Though citations are critical for communicating science and evaluating scholarly success, properties unrelated to the quality of the work-such as cognitive biases-can influence citation decisions. The primacy effect, in particular, is relevant to lists, which for in-text citations could result in citations earlier in the list receiving more attention than those later in the list. Therefore, how citations are ordered could influence which citations receive the most attention. Using a sample of 150,000 articles, we tested whether alphabetizing in-text citations biases readers into citing more often articles with first authors whose surnames begin with letters early in the alphabet. We found that surnames earlier in the alphabet were cited more often than those later in the alphabet when journals ordered citations alphabetically compared with chronologically or numerically. This effect seemed to be stronger in psychology journals (which have a culture of alphabetizing citations) compared with biology or geoscience journals (which primarily order chronologically or numerically) and was strongest among moderately and highly cited articles. Therefore, alphabetizing in-text citations biases citation decisions toward authors with surnames occurring early in the alphabet. These citation decisions result from an interaction between cognitive biases (more attention devoted to items earlier in a list) and the structure of the citation environment (the style in which citations are ordered). We suggest that journals using alphabetically ordered citations switch to chronological ordering to minimize this arbitrary alphabetical citation bias.