UCLA Graduate School of Education and Information Studies

As science becomes more dependent upon digital data, the need for data curation and for data digital libraries becomes more urgent. Questions remain about what researchers consider to be their data, their criteria for selecting and trusting data, and their orientation to data challenges. This paper reports findings from the first 18 months of research on astronomy data practices from the Data Conservancy. Initial findings suggest that issues for data production, use, preservation, and sharing revolve around factors that rarely are accommodated in use cases for digital library system design including trust in data, funding structures, communication channels, and perceptions of scientific value.

This article presents and validates a clustering-based method for creating cultural ontologies for community-oriented information systems. The introduced semiautomated approach merges distributed annotation techniques, or subjective assessments of similarities between cultural categories, with established clustering methods to produce cognate ontologies. This approach is validated against a locally authentic ethnographic method, involving direct work with communities for the design of fluid ontologies. The evaluation is conducted with of a set of Native American communities located in San Diego County (CA, US). The principal aim of this research is to discover whether distributing the annotation process among isolated respondents would enable ontology hierarchies to be created that are similar to those that are crafted according to collaborative ethnographic processes, found to be effective in generating continuous usage across several studies. Our findings suggest that the proposed semiautomated solution best optimizes among issues of interoperability and scalability, deemphasized in the fluid ontology approach, and sustainable usage.

Among modern digital technology elites, myths of meritocracy and intellectual prowess are used as racial and gender markers of white male supremacy that disproportionately consolidate resources away from people of color, particularly African Americans, Latino/as and Native Americans. Investments in meritocratic myths suppress interrogations of racism and discrimination even as the products of digital elites are infused with racial, class, and gender markers. Longstanding struggles for social, political, and economic inclusion for African Americans, women, and other legally protected classes have been predicated upon the recognition of systemic exclusion, forced labor, and structural disenfranchisement, and commitments to US public policies like affirmative action have, likewise, been fundamental to political reforms geared to economic opportunity and participation. The rise of the digital technocracy has, in many ways, been antithetical to these sustained efforts to recognize race and gender as salient factors structuring technocratic opportunity and inclusion. This chapter explores some of the ways in which discourses of Silicon Valley technocratic elites bolster investments in post-racialism as a pretext for re-consolidations of capital, in opposition to public policy commitments to end discriminatory labor practices. Through a careful analysis of the rise of digital technology companies, and a discussion of how technology elites work to mask everything from algorithmic to genetic inscriptions of race embedded in their products, we show how digital elites elide responsibility for their post-racial re-inscriptions of racial visibilities (and invisibilities). Using historical and critical discourse analysis, the chapter reveals how myths of a digital meritocracy premised on a technocratic colorblindness emerge key to perpetuating gender and racial exclusions.

Since the late 1970s, feminist media scholars have used the term “symbolic annihilation” to denote how strong women characters are absent, grossly under-represented, maligned, or trivialized by mainstream television programming, news outlets, and magazine coverage. In the wake of this absence, minoritized communities fail to see themselves or their place in the world. In archival studies, the concept of symbolic annihilation has recently has been used to describe the affective impact on the South Asian American community of being excluded, silenced or misrepresented in mainstream archival collections.  The proposed paper builds on and expands this research by examining the affective impact of both exclusion and representation in archives on members of communities that have coalesced around and been marginalized because of ethnic, racial, gender, sexual, and/or political identities. Based on more than a dozen in-depth qualitative interviews with practitioners at several independent community archives in Southern California—including those representing LGBTQ communities and communities of color—our research explores how symbolic annihilation operates and the affect it produces among archives users. We argue that independent, identity-based community archives can counter the symbolic annihilation of mainstream collections by providing avenues for minoritized communities to meaningfully represent themselves. We propose the term representational belonging to describe the ways in which such organizations enable people to have the power and authority to establish and enact their presence in ways that are complex, meaningful, substantive, and positive.

As science becomes more dependent upon digital data, the need for data curation and for data digital libraries becomes more urgent. Questions remain about what researchers consider to be their data, their criteria for selecting and trusting data, and their orientation to data challenges. This paper reports findings from the first 18 months of research on astronomy data practices from the Data Conservancy. Initial findings suggest that issues for data production, use, preservation, and sharing revolve around factors that rarely are accommodated in use cases for digital library system design including trust in data, funding structures, communication channels, and perceptions of scientific value.