Scaling Up and Scaling Down: Expanding and Contracting in the Move to Linked Data

Our project, Lesbian and Gay Liberation in Canada (LGLC, lglc.ca), has reached an exciting new phase: after creating 34,000 digital records documenting gay liberation in Canada from 1964 to 1981, we are expanding the project, both technologically and in scope. While popular histories of gay liberation in Canada tend to focus on the activism of gay, white, urban, anglophone men, the analysis of the data from the first six years of the LGLC project has shown that gay liberation in Canada was not solely driven by this demographic. This next phase of the LGLC project, started in 2020, combines archival research and linked data creation to represent the diversity and span of gay liberation organizing, intersectional personhood, and activist knowledge exchange, with a particular focus on women’s and francophone activism (Crenshaw; Sheffeild; Gevisser; Bilge). The end date of the original project chronology, 1981, marks the start of the AIDS crisis, a milestone in Canadian gay men’s organizing. This end date centres men’s experience and did not, as our critics have pointed out, help the project meet its goals of documenting patterns in women’s activism. We have expanded the project’s end date to 1985 to capture significant organizing, and resulting social and legal change, by lesbian mothers and trans women.

This expansion of the project coincides with a new phase of our digital scholarship: the conversion of its content, drawn from archival sources, to linked data. While the project uses a graph data model akin to RDF, the strictures of existing ontologies create a flattening effect, as they do on most data derived from archives. Moreover, while archival research may reveal how people understood their historical situation and the identities available to or forged by them, the archival record is not neutral (Roberto; Drabinski). In our conversion to linked data we take our cue from Klein and D’Ignazio’s call to preserve diverse (i.e. messy) data as a way to push back against statistical analysis’ eugenicist roots, roots that have historically shaped cultural attitudes towards queer and other marginalised people (Klein and D’Ignazio; Brown; Maxwell; Tanguay). In this short paper we will share the results of our experiments in modeling our person data in CIDOC-CRM, chosen for maximum interoperability, and in a combination of Schema.org and the CWRC ontology, to most closely match our original data structure.

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