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1. Youth invisible work: the sociocultural and collaborative processes of online search and brokering between adolescents and English-language learning families

   https://doi.org/10.1108/ILS-01-2022-0004  

   Yip, Jason ; Roldan, Wendy ; Gonzalez, Carmen ; Pina, Laura R. ; Ruiz, Maria ; Vanegas, Paola ( July 2022 , Information and Learning Sciences)

   This study aims to investigate the collaboration processes of immigrant families as they search for online information together. Immigrant English-language learning adults of lower socioeconomic status often work collaboratively with their children to search the internet. Family members rely on each other’s language and digital literacy skills in this collaborative process known as online search and brokering (OSB). While previous work has identified ecological factors that impact OSB, research has not yet distilled the specific learning processes behind such collaborations. Design/methodology/approach: For this study, the authors adhere to practices of a case study examination. This study’s participants included parents, grandparents and children aged 10–17 years. Most adults were born in Mexico, did not have a college-degree, worked in service industries and represented a lower-SES population. This study conducted two to three separate in-home family visits per family with interviews and online search tasks. Findings: From a case study analysis of three families, this paper explores the funds of knowledge, resilience, ecological support and challenges that children and parents face, as they engage in collaborative OSB experiences. This study demonstrates how in-home computer-supported collaborative processes are often informal, social, emotional and highly relevant to solving information challenges. Research limitations/implications: An intergenerational OSB process is different from collaborative online information problem-solving that happens between classroom peers or coworkers. This study’s research shows how both parents and children draw on their funds of knowledge, resilience and ecological support systems when they search collaboratively, with and for their family members, to problem solve. This is a case study of three families working in collaboration with each other. This case study informs analytical generalizations and theory-building rather than statistical generalizations about families. Practical implications: Designers need to recognize that children and youth are using the same tools as adults to seek high-level critical information. This study’s model suggests that if parents and children are negotiating information seeking with the same technology tools but different funds of knowledge, experience levels and skills, the presentation of information (e.g. online search results, information visualizations) needs to accommodate different levels of understanding. This study recommends designers work closely with marginalized communities through participatory design methods to better understand how interfaces and visuals can help accommodate youth invisible work. Social implications: The authors have demonstrated in this study that learning and engaging in family online searching is not only vital to the development of individual and digital literacy skills, it is a part of family learning. While community services, libraries and schools have a responsibility to support individual digital and information literacy development, this study’s model highlights the need to recognize funds of knowledge, family resiliency and asset-based learning. Schools and teachers should identify and harness youth invisible work as a form of learning at home. The authors believe educators can do this by highlighting the importance of information problem solving in homes and youth in their families. Libraries and community centers also play a critical role in supporting parents and adults for technical assistance (e.g. WiFi access) and information resources. Originality/value: This study’s work indicates new conditions fostering productive joint media engagement (JME) around OSB. This study contributes a generative understanding that promotes studying and designing for JME, where family responsibility is the focus.

    

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2. A Multi-match Approach to the Author Uncertainty Problem

   https://doi.org/10.2478/jdis-2019-0006  

   Carley, Stephen F. ; Porter, Alan L. ; Youtie, Jan L. ( June 2019 , Journal of Data and Information Science)

   Abstract Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage. 

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3. Privacy concerns with using public data for suicide risk prediction algorithms: a public opinion survey of contextual appropriateness

   https://doi.org/10.1108/JICES-08-2021-0086  

   Zimmer, Michael ; Logan, Sarah ( December 2021 , Journal of Information, Communication and Ethics in Society)

   Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question. 

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4. Examining the Literacy Practices of Engineers to Develop a Model of Disciplinary Literacy Instruction for K-12 Engineering (Work in Progress)

   Green, Theresa ; Minichiello, Angela ; Wilson-Lopez, Amy ( June 2018 , ASEE Annual Conference proceedings)

   Despite efforts to diversify the science, technology, engineering, and mathematics (STEM) workforce, engineering remains a White, male-dominated profession. Often, women and underrepresented students do not identify with STEM careers and many opt out of STEM pathways prior to entering high school or college. In order to broaden participation in engineering, new methods of engaging and retaining those who are traditionally underrepresented in engineering are needed. This work is based on a promising approach for encouraging and supporting diverse participation in engineering: disciplinary literacy instruction (DLI). Generally, teachers use DLI to provide K-12 students with a framework for interpreting, evaluating, and generating discipline-specific texts. This instruction provides students with an understanding of how experts in the discipline read, engage, and generate texts used to solve problems or communicate information. While models of disciplinary literacy have been developed and disseminated in several humanities and science fields, there is a lack of empirical and theoretical research that examines the use of DLI within the engineering domain. It is thought that DLI can be used to foster diverse student interest in engineering from a young age by removing literacy-based barriers that often discourage underrepresented students from entering and pursuing careers in STEM fields. This work-in-progress paper describes a new study underway to develop and disseminate a model of disciplinary literacy in engineering. During this project, researchers will observe, interview, and collect written artifacts from engineers working across four sub-disciplines of engineering: aerospace/mechanical, biological, civil/environmental, and electrical/computer. Data that will be collected include interview transcripts, observation field notes, engineer logs of literacy practices, and photographs of texts that the engineers read and write. Data will be analyzed using constant comparative analytic (CCA) methods. CCA will be used to generate theoretical codes from the data that will form the basis for a model of disciplinary literacy in engineering. As a primary outcome of this research, the engineering DLI model will promote the use of DLI practices within K-12 engineering instruction in order to assist and encourage diverse, underrepresented students to engage in engineering courses of study and pursue STEM careers. Thus far, the research team has begun collecting and analyzing data from two electrical engineers. This work in progress paper will report on preliminary findings, as well as implications for K-12 classroom instruction. For instance, this study has shed insights on how engineers use texts as part of the process of conducting failure analysis, and the research team has begun to conceptualize how these types of texts might be used with K-12 students to help them conduct failure analyses during design testing. Ultimately, this project will result in a list of grade-appropriate texts, evaluative frameworks, and activities (e.g., failure analysis in testing) that K-12 engineering teachers can use to prepare their diverse students to think, act, read, and write like engineers. 

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5. New speakers and language change in Diné Bizaad (Navajo)

   https://doi.org/10.1177/13670069221110400  

   Palakurthy, Kayla ( August 2022 , International Journal of Bilingualism)

   This paper analyzes the extent to which new speakers are participating in an ongoing phonological change in Diné Bizaad (Navajo). The implications of these patterns are discussed as they relate to theories of new speakers and language change.

   I apply a variationist methodology to analyze the pronunciation of lateral affricates from speakers representing different generations and language learning contexts. I focus on comparing new speakers, who report acquiring the language primarily through school or in a language program, with their age-equivalent peers.

   The data come from interviews recorded with 51 bilingual Diné Bizaad-English participants, ages 18–75. This includes four new speakers. The analysis focuses on variation in the lateral affricates in connected speech samples and an oral translation task.

   Results reveal that new speakers diverge from other younger participants in their lack of participation in an ongoing change in the affricates. Instead, new speakers more closely resemble middle-aged and older speakers.

   This study applies the new speaker framework to an Indigenous North American language, an under-represented sociolinguistic context within the literature. These findings provide a counterexample to the more frequent finding of new speakers linguistically diverging from older, traditional speakers.

   These results are interpreted as arising due to literacy practices, language usage networks, and community values. The orthographic representation of the affricates is thought to inhibit sound change. At the same time, due to their more formal language learning background, new speakers have developed a self-monitored speech style oriented toward the prestigious, older speakers. A lack of peer group language usage is thought to prevent the development of linguistically or ideologically distinct new speaker varieties. The confluence of these factors means that instead of constituting agents of language change, new speakers are more similar to older participants.

    

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