Trespassing the gates of research: identifying algorithmic mechanisms that can cause distortions and biases in academic social media

PurposeThe use of recommender systems is increasing on academic social media (ASM). However, distinguishing the elements that may be influenced and/or exert influence over content that is read and disseminated by researchers is difficult due to the opacity of the algorithms that filter information on ASM. In this article, the purpose of this paper is to investigate how algorithmic mediation through recommender systems in ResearchGate may uphold biases in scholarly communication.Design/methodology/approachThe authors used a multi-method walkthrough approach including a patent analysis, an interface analysis and an inspection of the web page code.FindingsThe findings reveal how audience influences on the recommendations and demonstrate in practice the mutual shaping of the different elements interplaying within the platform (artefact, practices and arrangements). The authors show evidence of the mechanisms of selection, prioritization, datafication and profiling. The authors also substantiate how the algorithm reinforces the reputation of eminent researchers (a phenomenon called the Matthew effect). As part of defining a future agenda, we discuss the need for serendipity and algorithmic transparency.Research limitations/implicationsAlgorithms change constantly and are protected by commercial secrecy. Hence, this study was limited to the information that was accessible within a particular period. At the time of publication, the platform, its logic and its effects on the interface may have changed. Future studies might investigate other ASM using the same approach to distinguish potential patterns among platforms.Originality/valueContributes to reflect on algorithmic mediation and biases in scholarly communication potentially afforded by recommender algorithms. To the best of our knowledge, this is the first empirical study on automated mediation and biases in ASM.

Could Government Data Openness Enhance Urban Innovation Capability? An Evaluation Based on Multistage DID Method

The wave of government data opening has gradually swept the world since it rose from the United States in 2009. The purpose is not to open government data, but to release data value and drive economic and social development through data accessibility. At present, the impact of academic circles on government open data mostly stays in theoretical discussion, especially due to the lack of empirical tests. Using the multistage difference-in-difference (DID) model, this paper analyzes the panel data from 2009 to 2016 by taking two batches of Chinese cities with open data released in 2014 and 2105 as samples to test the impact of government data opening on urban innovation ability. The results show that the opening of government data significantly improves urban innovation abilities. After considering the heterogeneity and fixed effects of urban characteristics, the opening of government data still significantly improves urban innovation ability and shows a greater innovation driving role in cities with high levels of economic development, human capital, and infrastructure. Based on this, this paper believes that we should continue to promote the opening of government data, release the value of data, and pay attention to the Matthew effect between cities that may appear in the era of big data.

Analysis of China’s Film Industry in 2020

The influence of the coronavirus disease 2019 pandemic impacted the global film market in 2020. Across the world, the Chinese film market was the first to recover and, as a result, assumed a leading position. This was because the government launched a return-to-work policy, the capital market became more rational, the integration of film companies accelerated, the film industry model trended toward centralization, and market structures underwent deep adjustments. Despite shrinking market space and declining film production during 2020, the industry produced films that remained diverse in genre and subject. Where the “Matthew effect” of accumulated advantage is much more acute in the film industry, a more diverse distribution approach has emerged in the field of new media. With box office returns approaching a ceiling, it has become more urgent to stabilize the quality of top films, enrich and enhance the competitiveness of genre films, and strengthen the theatricality of art films. It also became urgent to improve the film industry system, the product system, the market system, and the box office window system.

Research of Bibliographic References in Math-Net.Ru using the Journal Citation Graph

According to the portal Math-Net.Ru a graph of journal citation is constructed. To increase the reliability of the model, the citation time interval was chosen from 2010 to 2021, when the distribution of citation articles stabilized at the level of 3500-4500 citations per year. The structure of link aging is studied and it is shown that their half-life is equal to 8 years. Therefore, the publication date of the cited articles was limited to 2002. For the constructed citation graph, the main properties, such as a small diameter and a high density, are obtained, indicating a high level of scientific communication in the Math-Net.Ru. The absence of the “Matthew effect” as a pronounced advantage in quoting established journals in relation to less well-known ones is shown. Adequacy of the journal citation graph Math-Net.Ru as a model of scientific communication confirmed by comparing the ranking of journals in the citation graph with their SCIENCE INDEX rating in eLIBRARY.RU. A direct moderate relationship between the two rankings is shown. A number of meaningful conclusions are drawn from the analysis of the citation graph.

Opening the black box of recurrence in innovation policy

This paper studies the role that the three theoretical sources of recurrence – the Matthew effect – play in the process of the first and recurrent granting of innovation public funds. Those sources are a firm’s “reputation”, “innovation capabilities” and “formulation capabilities”. The empirical analysis is based on the Argentinean Technological Fund (in Spanish, FONTAR) between 2007-2018. The results show that firms’ formulation skills increase the probability of funds initially being granted, and then additional formulation skills and innovation capabilities increase the probability of recurrence, while reputation does the opposite.

Contributions of declarative memory and prior knowledge to incidental L2 vocabulary learning

The bulk of second language (L2) vocabulary learning happens incidentally through reading (Rott, 2007; Webb, 2008), but individual differences, such as prior knowledge, modulate the efficacy of such incidental learning. One individual difference that is strongly predicted to play a role in L2 vocabulary is declarative memory ability; however, links between these two abilities have not been explored (Hamrick, Lum, & Ullman, 2018). This study considered declarative memory in conjunction with varying degrees of prior knowledge, since declarative memory may serve a compensatory function (Ullman & Pullman, 2015). L2 Spanish learners completed measures of prior Spanish vocabulary knowledge, declarative memory ability, and incidental L2 vocabulary learning. The results suggest that better declarative memory predicts better immediate learning in general and better vocabulary retention two days later, but only for those with more prior knowledge, consistent with the Matthew Effect previously reported in the literature (Stanovich, 1986).

The Matilda Effect

This chapter suggests that the most important factors that diminished Esther Lederberg’s scientific career and legacy were her gender and marriage. The fact that her famous collaborator was also her husband doubled the chances that her own scientific achievements were overshadowed. The chapter goes on to explain how the so-called Matthew and Matilda Effects altered the history of science right at birth of genetics as a distinct branch of biology. As an example of the Matilda Effect, the chapter presents Nettie Stevens whose discovery of the XY sex-determining chromosomes in 1905 and establishment of the two patterns of sex chromosomes in various beetles, flies, and bugs was credited to Edmund Wilson, a better-known scientist. In an example of the Matthew Effect, Thomas Hunt Morgan, the most famous geneticist of the early twentieth century, eventually received most of the credit for discovering sex chromosomes. Finally, the careers and legacies of three other Matildas who worked in the early days of microbial genetics—Martha Chase, Laura Garnjobst, and Daisy Dussoix—are presented.