Assessing the Diversity of the E-collection of the William H. Hannon Library

Purpose. The American Library Association’s 1982 statement[1] on Diversity in Collection Development reminds librarians of the professional responsibility “to select and support the access to materials on all subjects that meet, as closely as possible, the needs, interests, and abilities of all persons in the community the library serves. This includes materials that reflect political, economic, religious, social, minority, and sexual issues.” In an effort to ensure that the collection of the William H. Hannon Library (of Loyola Marymount University, Los Angeles, California, USA {LMU}) aligns with its institutional vision[2] (including “bridging disciplines” and “representing diverse topics and perspectives”) and meets the research needs of a diverse campus population, a team of library staff has designed a project to assess the library’s electronic collection through the lens of diversity. While some similar studies have been done at larger research institutions (notably that of Ciszek and Young (2010)),[3] this project further interrogates inclusivity in database collections and integrates LMU student learning into the research process. The results of the evaluation will inform the library collection strategy and ensure that collections are built that deliberately and positively contribute to an inclusive campus climate. Methods. Guided by the project team, a select group of library student employees will be working in spring 2018 to use a series of pre-determined keyword phrases to search through about two-hundred of the LMU library’s databases. The project team will code the keyword phrases into categories of diversity, so that the library can better understand its e-resource collection. Categories like Disability, People of Color, and Gay, Lesbian, Bisexual, and Transgender will be used to determine if content is well represented in the collection, particularly compared to the vendor supplied description. In addition to conducting the keyword phrase searches, the student evaluation team will respond to reflective prompts along the way, answering questions like, “Based on the search results, do you consider the database to be ‘diverse’?” and “Would you recommend this database to someone doing research about diversity or inclusion, in your major? Why or why not?” Findings and Value. The project team is anticipating the completion of the student evaluation, so that they can determine if the library collection is diverse, or if there are gaps in the electronic resource collection that can be strategically filled. The process and results of this study will be shared as broadly as possible, with an outline of subsequent actions to be taken. [1] https://www.ala.org/Template.cfm?Section=interpretations&Template=/ContentManagement/ContentDisplay.cfm&ContentID=8530 [2] http://library.lmu.edu/aboutthelibrary/libraryvisionmission/ [3] Ciszek, Matthew P., and Courtney L. Young, (2010), “Diversity collection assessment in large academic libraries”, Collection Building, Vol. 29 Iss 4 pp. 154 – 161

A Survey of Copyright Workflows for Institutional Repositories (poster session)

Many institutional repositories (IRs) provide open access to published work. The authors of those works typically transfer copyright or exclusive distribution rights for their work to their publisher. This means the author cannot themselves grant permission for deposit in the IR; rather permissions must be secured from the publisher. In Fall 2009, the authors conducted a survey of institutional repository managers to gain a clearer understanding of the staffing, resources, activities and tools employed to clear copyright for published work, with the intent to deposit into an IR. This poster summarizes preliminary findings

Redefining Library Partnerships: Sharing Physical and Digital Space with the Campus Community

Cal Poly State University librarians are engaging faculty, staff and students by transforming physical and digital library spaces to better support teaching and learning. A Science Café program hosted in the Learning Commons Library Café provides informal opportunities to come together over coffee, share current faculty research, and make salient the connections that exist between the numerous and seemingly unrelated areas of study on campus: science, humanities and the social sciences. The campus institutional repository (IR), DigitalCommons@CalPoly, is an ever-growing digital archive of faculty research, student work and campus documents which has facilitated new collaborations between faculty, campus constituents and the library and has elevated the visibility of faculty and student research. Sharing of physical and digital space provides for dynamic, campus-centered programs and initiatives bringing together technology, information, and people to create a myriad of connections. These initiatives are redefining physical and digital library spaces, catalyzing renewed interest in the library and fostering communication and connections on campus

A Comparative Study of Bibliometric Characteristics of Competitive Intelligence Scholarly Material in Business and Library Science Databases

This study identifies and examines bibliometric characteristics that differentiate Competitive Intelligence scholarly literature found in the ABI/Inform Complete and Library and Information Science Abstracts (LISA) databases from January 1975 to December 2004. The term "competitive intelligence" is the most popular term to use when searching for CI scholarly materials. The journal distribution for both databases follows Bradford's law of scatter, but there is little overlap between the core journals and core authors identified in ABI/Inform and LISA, indicating the need for multiple searches across databases. The erratic growth patterns and inconsistent subject indexing point to database collection inconsistencies. Overall, ABI/Inform yielded more articles relating to the field of competitive intelligence than LISA

A Q model investment system in material and immaterial assets

The aim of this paper is to study the determinants of investment on material and immaterial assets of Spanish firms considering they are heterogeneous assets. With this objective, we propose an investment model based on both prospective models and Tobin’s q. The developed model is then applied to analyse the investment determinants of material and immaterial assets over a panel of 87 non-financial Spanish firms that have been quoting on the Stock Market during 12 years. Results show that material investment decisions are isolated from immaterial assets, whereas immaterial investment is affected by tangible investment and stock.This paper was developed under the objectives of the CREVALOR Research Group (DGASpain). It was financed by MEC-FEDER Research Project SEJ2005-07341.peer-reviewe

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Bancos y otras entidades financieras

El lavado de dinero es una problemática que perjudica no sólo a una nación sino también a todo el mundo, dadas las consecuencias que este delito trae aparejadas en la economía, el gobierno y el bienestar social de las naciones, implicando además una grave amenaza para la seguridad nacional, regional e internacional. El siglo XXI nos sorprende con nuevas herramientas tecnológicas y métodos accesibles para llevar a cabo transacciones en efectivo o electrónicas de dinero permitiendo que las principales organizaciones delictivas puedan concretar delitos de lavado de activos y financiamiento del terrorismo. Actualmente, las transferencias de dinero obtenido de actividades ilícitas no poseen impedimentos de fronteras políticas, económicas o sociales. Asimismo, la globalización, las relaciones internacionales y la creciente interdependencia económica entre países brindan mayores oportunidades y nuevos mecanismos para lavar dinero y legitimar toda clase de fondos de origen ilegal. A lo largo del presente trabajo, se abordan las siguientes cuestiones vinculadas al proceso de lavado de activos: su definición, etapas que comprende, tipologías que adopta y consecuencias económicas, sociales y políticas que produce. Asimismo, se pretende identificar los principales organismos internacionales y nacionales involucrados en la prevención de lavado de activos y financiamiento del terrorismo. Particularmente, la presente investigación se centra en dar a conocer cuál es la normativa vigente en la República Argentina aplicable a las entidades financieras, sujetos obligados a informar operaciones sospechosas de lavado de dinero según la ley 25.246. Las mencionadas entidades, a su vez deberán cumplir disposiciones de la Unidad de Información Financiera y del Banco Central de la República Argentina.Fil: Maccari, Estefania Lourdes . Universidad Nacional de Cuyo. Facultad de Ciencias Económicas.Fil: Martin, Denis Nicolás . Universidad Nacional de Cuyo. Facultad de Ciencias Económicas.Fil: Ramirez, Marisa Gisel. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas

Bacteremia caused by an Acinetobacter junii strain harboring class 1 integron and diverse DNA mobile elements

INTRODUCTION: Infections caused by Acinetobacter junii are rarely reported. However, some outbreaks of septicemia in neonates and pediatric oncology patients, as well as meningitis, peritonitis, and ocular infection have been described. Since it is highly infrequent to find the molecular characterization of A. junii strains in literature, in this study we described the molecular characterization of A. junii isolates recovered from blood samples of a renal transplant patient. METHODOLOGY: The case was defined as a catheter-related bacteremia caused by A. junii. The patient responded favorably after catheter removal and treatment with ciprofloxacin. RESULTS AND CONCLUSION: The complete molecular characterization of the isolate showed that it harbored a class 1 integron and diverse DNA mobile elements. This explains its genomic plasticity for acquiring antimicrobial resistance determinants and for adapting to a nosocomial niche.Fil: Traglia, German Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Almuzara, Marisa. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vilacoba, Elisabet. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Tuduri, Alicia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos ; ArgentinaFil: Neumann, Gabriela. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos ; ArgentinaFil: Pallone, Elida Carmen. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos ; ArgentinaFil: Centron, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Ramirez, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; Argentin

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys