Short Abstract

How can singular records, marginal voices, and “small data” challenge knowledge produced through aggregation? This panel explores the methodological and analytical potential as well as ethics and responsibility of research with singularities, underrepresented sources and situated forms of knowledge.

Long Abstract

Contemporary knowledge production increasingly privileges aggregation, comparability and empiricism: individual experiences become variables, variables become types, and statistical significance can eclipse questions of meaning. Data are cleaned, classified, optimised and interpreted through methodological conventions and increasingly through algorithms. Yet what counts as evidence—and what remains invisible/unheard—depends on who collects, structures and interprets the data. Those who contribute data are rarely those who ultimately produce knowledge from it.

This panel asks what comes into view when we turn our attention to the margins: to singular records, fragmentary archives, situated testimonies, linguistic and cultural idiosyncrasies, and sources produced by or with communities that are statistically and/or socially marginalised. Such sources may appear methodologically inconvenient precisely because they resist aggregation, categorisation or replication. Yet, like a prism, they can refract seemingly coherent patterns and reveal spectra of experience and wisdom obscured by dominant analytical frames.

We invite contributions that critically engage with singularities in the data pool and with the wisdom bound in lesser known records, alternative interpretations and translations, which may have the potential to challenge methodological orthodoxy, unsettle established paradigms, and which attend to unheard voices. We are particularly interested in reflexive, collaborative and deconstructive approaches that respond to positionality, context and the agency of knowledge contributors. How can singularity become analytically productive without being romanticised? How might unique artefacts expose absences or biases in datasets and interpretations? And how can researchers create knowledge with, rather than merely about, those whose experiences remain at the margins of the data?

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