The effects of governments on management and organization

We review and integrate existing research from organization theory, strategy, organizational behavior, economics, sociology and political science on the effects of governments on organization and management, with a focus on how governing ideology and government capability influence independent organizations’ forms, strategies, and their participants’ behavior. When brought together these works suggest significant research opportunities in the fields of management and organization, as well as new perspectives on public policy challenges. Several avenues of potentially profitable empirical research include more attention to the influence of government on corporate strategies, more research on the strategies of pursuing corruption and government capture for competitive advantage, the role of government in fostering innovation and the growth of entrepreneurial organizations, and extra‐organizational contextual effects on managerial and employee organizational behavior. Possible public policy implications are illustrated with an application to the role of organizations in national wealth generation and dispersion

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press