Questions asked at the virtual and physical health sciences reference desk: how do they compare and what do they tell us?

The questions asked at the traditional reference desk are decreasing while questions asked at the virtual reference desk are on the rise. Over a one-month period, the types of reference questions asked at an academic health sciences library were coded. This paper examines and compares the types of questions asked at the current day reference desk versus the virtual reference desk. This paper also reviews past literature examining the types of questions asked via virtual reference and the traditional reference.     

Digital repositories ten years on: what do scientific researchers think of them and how do they use them?

Digital repositories have been with us for more than a decade, and despite the considerable media and conference attention they engender, we know very little about their use by academics. This paper sets out to address this by reporting on how well they are used, what they are used for, what researchers' think of them, and where they thought they were going. Nearly 1,700 scientific researchers, mostly physical scientists, responded to an international survey of digital repositories, making it the largest survey of its kind. High deposit rates were found and mandates appear to be working, especially with younger researchers. Repositories have made significant inroads in terms of impact and use despite, in the case of institutional repositories, the very limited resources deployed. Subject repositories, like arXiv and PubMed Central, have certainly come of age but institutional repositories probably have not come of age yet although there are drivers in place which, in theory anyway, are moving them towards early adulthood.     

A qualitative content analysis of watchlists vs safelists: How do they address the issue of predatory publishing?

Predatory journals and publishers are a growing concern in the scholarly publishing arena. As one type of attempt to address this increasingly important issue, numerous individuals, associations, and companies have begun curating journal watchlists or journal safelists. This study uses a qualitative content analysis to explore the inclusion/exclusion criteria stated by scholarly publishing journal watchlists and safelists to better understand the content of these lists, as well as the larger controversies that continue to surround the phenomenon that has come to be known as predatory publishing. Four watchlists and ten safelists were analyzed through an examination of their published mission statements and inclusion/exclusion criteria. Notable differences that emerged include the remaining influence of librarian Jeffrey Beall in the watchlists, and the explicit disavowal of his methods for the safelists, along with a growing recognition that the “list” approach may not fully address systemic aspects of predatory publishing that go beyond the individual author's ethical decision-making agency.     

How do public libraries create social capital? An analysis of interactions between library staff and patrons

In this qualitative study exploring the content of social interactions between library staff and patrons, interviews were held with 15 library staff members in three neighborhood branch libraries in a large American midwestern city. An analysis of the interviews suggests that public libraries may contribute to social capital through the relationships and interactions that occur between staff and patrons. Some of the ways in which these relationships and interactions may contribute to social capital include: building patrons' trust in the library and its staff, connecting people to both community and library resources, providing social support for patrons, reducing social isolation, helping patrons gain skills to function in an increasingly online world, and providing a positive place for neighborhood residents to gather. The kinds of social interactions occurring in libraries that may help to build social capital are highlighted.     

Who posts scientific tweets? An investigation into the productivity,locations, and identities of scientific tweeters

Twitter altmetrics has been proposed to measure the popularity and the potential societal impact of scientific products, but scientific tweeters who produce the Twitter altmetrics data have not been well explored. The study, by analyzing 2.63 million scientific tweeters’ data that are extracted from the Altmetric.com company dataset, is aimed to reveal their productivity and geographic distribution in a comprehensive way. To gain a more in-depth understanding of their account types and identities, 1468 scientific tweeters of different levels of activeness are sampled for further analysis. Our results show that: (1) The extent to which a small proportion of tweeters have posted most of scientific tweets increases over time. In 2016, 10% of scientific tweeters have posted 80% of scientific tweets; (2) Scientific tweeters are widely distributed around the world but in a different pattern with the distribution of general Twitter users. In addition, scientific tweeters are found to be more active in tweeting scientific products than retweeting them in certain areas. (3) Manual coding of the sampled tweeters shows that the percentage of bot accounts among scientific tweeters is 1.8%, which is much lower than that among general Twitter users. Moreover, 73% of scientific tweeters use Twitter for professional purpose, 76% use real names for their accounts, and 16% are institutional accounts. (4) Identities of scientific tweeters are diversified. 49% of them are researchers among which university faculty is the major type, and 38% of them are the general public. With these results we suggest number of scientific tweets is not a good indicator of measuring either popularity or impact, tweeter’s productivity, location and identities must be taken into consideration in interpreting the meaning of Twitter altmetrics.     

How do fans purposively create information to promote a celebrity? An analysis of fans' information practices and literacy improvement

Fans purposively creating information aiming specifically to inform the public positively about a celebrity has become a significant practice within contemporary Chinese fan culture. “Celebrity promotion” information creation offers an important opportunity to understand how people intentionally create information beyond internal user groups and existing information needs. Based on unobtrusive observation and semi-structured interviews, findings show that fans create positive online content and conduct offline advertisements to boost a celebrity's specific works, reputation, and fan base. Fans also intentionally develop strategies and improve their information literacy to make informational materials more visible and appealing, while critically separating “celebrity promotion” from enjoyment-centered fandom activities. As fans adapt to the current information environment, the intentionality of their practices stems from beliefs that 1) they are the actor(s)’ shareholders/partners and 2) by creating “promotion” information, fans can achieve their goal of helping the actor(s).     

What do clinicians want from us? An evaluation of Brighton and Sussex University Hospitals NHS Trust clinical librarian service and its implications for developing futureworking patterns

Background: The Clinical Librarian (CL) Service at Brighton was established in 2003 with the aim of providing high‐quality evidence to designated teams and fostering an evidence‐based culture. Objective: To evaluate the CL service at Brighton and discuss the implication of the findings. Methods: A combination of internally collected data (n = 167), and an external evaluation of the service by questionnaires (n = 86) of users and non‐users and interviews (n = 9) of users. Results: Internal data suggest that the service is valued by its users and that patient care and continuing professional development are the most common uses for searches (confirmed by the external study); that searches generally result in some change in knowledge; and that this knowledge is disseminated. The external study found that visibility of the CL was crucial to the effectiveness of the role and that clinicians used the service mostly to get access to a wider range of resources and/or to save time. Users wanted the CL to include evaluative annotation with the results, and for the CL role to become more embedded in the team. Interview results expanded on the issues of integration of the CL and the need for annotation of results. Conclusions: To be most effective, CLs would be dedicated to one team, but financial constraints make this unlikely. Alternative working patterns are suggested as a possible compromise.     

How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects

Evaluative bibliometrics is concerned with comparing research units by using statistical procedures. According to Williams (2012) an empirical study should be concerned with the substantive and practical significance of the findings as well as the sign and statistical significance of effects. In this study we will explain what adjusted predictions and marginal effects are and how useful they are for institutional evaluative bibliometrics. As an illustration, we will calculate a regression model using publications (and citation data) produced by four universities in German-speaking countries from 1980 to 2010. We will show how these predictions and effects can be estimated and plotted, and how this makes it far easier to get a practical feel for the substantive meaning of results in evaluative bibliometric studies. An added benefit of this approach is that it makes it far easier to explain results obtained via sophisticated statistical techniques to a broader and sometimes non-technical audience. We will focus particularly on Average Adjusted Predictions (AAPs), Average Marginal Effects (AMEs), Adjusted Predictions at Representative Values (APRVs) and Marginal Effects at Representative Values (MERVs).