Linkedin,platforming labour,and the new employability mandate for universities

ABSTRACT

Students, academics and university administrators are increasingly using and producing digital platforms, including social media. This paper focuses on LinkedIn to start tackling the question of the effects on higher education as a sector, its actors and the established social practices. It argues that LinkedIn moves beyond the passivity of advertising to its users towards actively structuring digital labour markets, in which it strategically includes universities and its constituents. By introducing the term ‘qualification altmetrics’, the paper suggests that LinkedIn is building a global marketplace for skills to run in parallel to, or instead of university degrees. Qualification altmetrics might challenge the established practices of knowledge production and valuation.     

Librarians’ Use of Images on LibGuides and Other Social Media Platforms

This study compared the use of librarians’ profile images across Web platforms designed for librarian-patron communication. The primary focus was LibGuides profiles at a peer group of Association of Research Libraries institutions. Librarians are currently using a variety of options, including professional head shots, casual head shots, other photos, alternate images, avatars, and no image. Where possible, results were also analyzed by gender. For a smaller selection of libraries, LibGuides photos were compared with Facebook photos to see if different images were used for a professional versus a personal social media setting. This research was done in December 2010 and duplicated in May 2012 to track changes over time. The later study also reviews profile images for the smaller selection of libraries in LinkedIn and ALA Connect. The findings provide a baseline for further comparative research, could also inform individual librarians’ image choices based on patterns among their peers, and gives the profession a starting point for discussions about the types of images librarians use.     

学术图书馆数据馆员职业发展状况的调查与分析

[目的/意义] 探索采用职业社交网络数据进行员工导向型工作分析的可能性,为加强我国图书馆人力资源开发、数据馆员教育培训和职业生涯规划提供参考。[方法/过程] 构建基于LinkedIn的数据馆员工作分析模型,通过数据挖掘和可视化展示分析数据馆员岗位分布、发展阶段、教育背景、职业技能和职业成长轨迹。[结果/结论] 研究表明,美国的数据馆员从业人数和设置该职位的机构数量最多。以拥有博士硕士学位者为主的数据馆员群体呈现明显的多学科特征,更加强调具备数据管理和分析等相关技能。专业学位教育和在线开放教育成为数据馆员知识能力培养的两种途径。从职业经历上看,稳定和良好的职业前景使数据馆员成为极具发展前途的职业。     

Researching graduate destinations using LinkedIn: an exploratory analysis of South African chemical engineering graduates

Surveys of graduate destinations typically suffer from relatively low response rates and therefore this study sought to develop a new methodology for tracking graduate destinations using LinkedIn. Research in this area with engineering graduates has also been limited, and thus this study, focusing on chemical engineering graduates from the University of Cape Town, brings new insights to a debate usually characterised by untested assumptions. Following the graduating cohorts from 2006 to 2012, it was shown that about a quarter go on to postgraduate studies. Of those that proceed to the workplace, the vast majority go into traditional graduate chemical engineering jobs in operations, mostly in the petrochemical and mining sectors in this South African context. The study sought to determine the potential impact of the 2008 recession on these graduate destinations but the results are inconclusive in that regard.     

Utilizing data driven methods to identify gender bias in LinkedIn profiles

The growing use of Artificial Intelligence-enabled recruitment systems has become an important component of modern talent recruitment, particularly through social networks such as LinkedIn and Facebook. However, data overflow embedded in recruitment systems, based on Natural Language Processing (NLP) methods, may result in unconscious gender bias. The purpose of this work is to utilize a set of methods to analyze and detect textual bias in different groups. We analyzed a training dataset of fourteen thousand LinkedIn profiles, which was provided by a company named Talenya, and included job-candidates that fit IT-related positions. We aimed to detect textual self-presentation gender gap patterns, utilizing Term Frequency - Inverse Document Frequency (TF-IDF), word2vec and the Universal Sentence Encoder (USE) to code the data, and applied the kernel two-sample test for the purpose of determining whether men’s and women’s LinkedIn profiles have the same distribution. Additionally, we focused on quantifying and identifying repetition in skills representation in the LinkedIn profile by applying the TF-IDF and cosine similarity tools and compared the repetitiveness pattern of men’s and women’s profiles. Gender-based analysis was also carried out on smaller, more homogeneous groups of candidates, who share the same position type, geographical location and organizational seniority level. Finally, we provide theoretical and practical implications.