Authenticating Hafu Identities on Instagram: A Small Stories Analysis of Interactions on Hafugods
Bibliographic record
Abstract
Described as “the predominant narrative environment for contemporary storytellers” (Mäkelä & Meretoja, 2022), social media are attracting increasing attention as sites for the study of a wide range of everyday narrative practices. Moreover, storytelling has become integrated into platform architectures through features such as Stories on Instagram, Snapchat and Facebook, and is being celebrated as “the ideal vehicle for presenting an authentic self” (Georgakopoulou, 2022). The word authenticity finds its roots in the Greek authentikós, (autós, self) and is associated with notions such as realness, genuineness, credibility and truthfulness (Lacoste, Leimgruber & Breyer, 2014, p2). In interactional sociolinguistics, authenticity is treated as an effect of discursive performance and negotiation of identity between participants in an interaction. The analytical focus is therefore on the discursive strategies and interactional processes that authenticate a person’s claim to a particular identity or group membership. This chapter explores the implications of authenticity as discursive performance in relation to hafu (ハーフ) identities, through a Small Stories (Georgakopoulou, 2013; 2016) analysis of an Instagram account called hafugods. Hafu is a racialised identity term used to describe someone who is mixed-ethnic or mixed-race Japanese, and hafugods presents itself as a space for telling stories about what it means to be hafu. The analysis demonstrates how storytelling is being mobilised for the performance of hafu identities, and reveals how alignment strategies such as ‘ritual appreciation’ and ‘knowing participation’ (Georgakopoulou, 2016) serve as authenticating practices of proving membership to the group, and by extension, a hafu identity. Furthermore, it draws on the concept of ‘enoughness’ (Blommaert & Varis, 2013) – premised on the idea that identities are constructed through discursive orientations towards emblematic features of particular identities that are configured in specific arrangements to emphasise ‘authenticity’ – to illustrate how iterative interactional practices and semiotic resources become established as emblematic of authentic hafu identities in this particular space. Through these analyses, this chapter provides a qualitative account of “how diasporic and transnational people…create discourse spaces in which to articulate marginal voices, negotiate plural identities, construct the meanings and boundaries of community” (Androutsopoulos & Juffermans, 2014, p3), and contributes to the ongoing exploration of the role of social media in mediating collective identifications, constructions of counternarratives and reimagining group identities (Bonilla & Rosa, 2015).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".