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Public libraries with multilingual communities should provide adequate access to multilingual resources to meet the needs of community members. Few previous studies have addressed online access to multilingual collections of public libraries that serve multilingual communities. This article highlights challenges in supporting multilingual users on public library websites that have multilingual users by asking what factors are involved, how the problems will eventually be solved, and what features will be needed in website design and indexing to enhance multilingual support. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to discuss deployment of cultural warrant in intercultural environment, aiming to better achieve ethical warrant. Design/methodology/approach: This paper synthesizes research on cultural warrant and classification, and uses examples of the Dewey Decimal Classification (DDC) to illustrate cultural warrant in a case of cross-cultural adaptation of bibliographic classification. Findings: The notion of intercultural warrant was suggested as an operational approach to cultural warrant in the context of intercultural use of Knowledge Organization System (KOS). Research limitations/implications: The research focuses on discussions of cultural warrant in the context of intercultural uses of KOS but lacking diverse examples of KOS and beyond (such as descriptive metadata standards). Originality/value: This paper suggests the development of intercultural warrant as a theoretical view to understand classification systems commonly used worldwide and a path to achieve ethical treatments of cultures in such systems. [ABSTRACT FROM AUTHOR]

Teleconnection in atmospheric science refers to a significant correlation between climate anomalies in widely separated regions (typically thousands of kilometres), and it is often considered to be responsible for extreme weather conditions occurring simultaneously over large distances. In this paper, we study the influence of El Niño- Southern Oscillation teleconnection on meteorological droughts represented by the Palmer severity drought index across North America from 1870 to 1990. We develop a flexible statistical framework based on spatial random effects to model the covariance (teleconnection) between winter (October--March) sea surface temperature in the tropical Pacific and summer (June--August) droughts in North America. Our model allows us to analyse the dynamic pattern of teleconnection over space and time, and results indicate that the influence of El Niño-Southern Oscillation teleconnections on droughts varies spatially and temporally across North America. We further provide the timevarying teleconnection estimates with their uncertainties for 12 subregions in North America. [ABSTRACT FROM AUTHOR]

Covariance structure modeling plays a key role in the spatial data analysis. Various parametric models have been developed to accommodate the idiosyncratic features of a given dataset. However, the parametric models may impose unjustified restrictions to the covariance structure and the procedure of choosing a specific model is often ad hoc. To avoid the choice of parametric forms, we propose a nonparametric covariance estimator for the spatial data, as well as its extension to the spatio-temporal data based on the class of space-time covariance models developed by Gneiting (J. Am. Stat. Assoc. 97:590–600, 2002 ). Our estimator is obtained via a nonparametric approximation of completely monotone functions. It is easy to implement and our simulation shows it outperforms the parametric models when there is no clear information on model specification. Two real datasets are analyzed to illustrate our approach and provide further comparison between the nonparametric estimator and parametric models. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand users' motivations and intentions in the use of institutional collections on social tagging sites. Previous social tagging studies have collected social tagging data and analyzed how tagging functions as a tool to organize and retrieve information. Many studies focused on the patterns of tagging rather than the users' perspectives. To provide a more comprehensive picture of users' social tagging activities in institutional collections, and how this compares to social tagging in a more personal context, we collected data from social tagging users by surveying 7,563 participants in the Library of Congress's Flickr Collection. We asked users to describe their motivations for activities within the LC Flickr Collection in their own words using open-ended questions. As a result, we identified 11 motivations using a bottom-up, open-coding approach: affective reactions, opinion on photo, interest in subject, contribution to description, knowledge sharing, improving findability, social network, appreciation, personal use, and personal relationship. Our study revealed that affective or emotional reactions play a critical role in the use of social tagging of institutional collections by comparing our findings to existing frameworks for tagging motivations. We also examined the relationships between participants' occupations and our 11 motivations. [ABSTRACT FROM AUTHOR]

As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians' toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using "black-boxes" and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics. [ABSTRACT FROM AUTHOR]