The myth of lesbian generation loss: Finding intergenerational solidarities in digital sexual selfhood projects
Bibliographic record
Abstract
The contemporary preoccupation with lesbian's potential obsolescence relies on implicit assumptions about the (ir)relevance of lesbian feminism to younger generations. In this article, we use the metaphor of "generation loss" to conceptualize the Gordian knot of affective and ideological ties that lie beneath this preoccupation. Contrary to the narrative of generation loss, we show how young people have begun to take up and share lesbian feminist concepts on social media platforms like TikTok. They do so in the name of resituating lesbian as a political project rather than an exclusionary demographic category. Instead of painting over lesbian feminism with the broad brushes of whiteness and trans-exclusivity, some young lesbians seek out other voices in the archive to debate whether and how this history might be recuperated as a challenge to white, cisnormative heteropatriarchy. Far from finding irrelevance, many revisiting lesbian feminism are excited to discover theories of gender, sexuality, and power that can be adapted to relocate lesbian to more durable and less essentializing territory than its current, narrowly biopolitical home. This presents a crucial opportunity to build bridges across generations and collectively resist the cooptation of lesbians as agents of white supremacist and transphobic political agendas.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".