'Doing love' online : performative gender and the urban everyday
Bibliographic record
Abstract
While much has been said about the role of online dating in transforming the nature of intimate relationships and love in Canada and beyond (Bauman, 2003; Ben-Ze'ev, 2004; Brym and Lenton, 2001) there has been no systematic study of the pursuit of hetero-romantic love online as a practice of both the everyday and of gendered selfing. In January 2007, I began an eight-month investigation into the everyday practices of urban professionals online dating in Vancouver, Canada to study what role new media play in producing particular kinds of gendered selves through the pursuit of love online. By engaging with critical readings of feminist theories to explore the ways love has evolved as a theoretical concept and an enduring, increasingly technologically-mediated social practice, I forward the concept of 'doing love' as a contemporary way individuals perform gender online. I argue that the pursuit of hetero-romantic love, that is, 'doing love' online, is a contemporary gendered selfing project that is both individualized and routinized as part of a larger gendered discursive field that seeks to position heterosexuality, as tied to hetero-romantic love, as natural, necessary, and inevitable. Gendered selfing, through the pursuit of hetero-romantic love, requires coherence and approval by others and is in this sense policed. I demonstrate how this policing is apparent in the online dating practices of my participants which include filtering, fat phobia and fat authenticity, the management of inappropriate aphrodisia, and contingent constructions of properly made 'homes' as the outcome or triumph of heterosexual online dating pursuits. Gender emerges in this study as a by-product of the regulatory force of constitutive hetero-romantic love pursuits that necessitate appropriately gendered bodies and being deemed suitable for heterosexual coupling. By exploring love as a performative and orienting force that is uniquely articulated through the performance of gender online, this study enriches understandings of gendered practices of selfing, as realized through engagement with new media. It thus illustrates the enduring and persistent nature of gender as an organizing, and at times oppressive, force in our everyday lives.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".