FEMME LIFE ON THE SCREEN: ONLINE METHODS FOR SUBCULTURAL RESEARCH ANDSURVIVAL
Bibliographic record
Abstract
In this presentation, I will share key findings from my dissertation research on femme internet culture. Following the conference theme, this presentation will focus on the use of online methods for documenting subcultural life, and online subculture’s ability to make life more liveable for marginalized subjects. In this project, I define “femme” as a queer identity that is marked by a critical and political engagement with femininity that manifests through one’s style and values. I used Ulrika Dahl’s (2011) femme-inist ethnography methodology to conceptualize a study of “one’s own community.” In this presentation, I will focus on key findings about femme memes and online femme networks. My research demonstrates that through a study of a subculture’s memes, we can come to learn much about the group’s values, norms, and boundaries. Memes allow individuals to see one’s self, identity, or experiences reflected, or be “in on the joke.” Femmes recognize the experiences specific to femme subjectivity (ie. femme invisibility) communicated through memes and feel a sense of connection with one another. In addition, my research offers further evidence of the value of online communities for marginalized subjects. The femmes in my study used online connections to combat geographical isolation, create intergenerational bonds, and even find a reason to stay alive. My research shows that the technological affordances of Instagram continue to make online communities valuable. In addition, online methods are valuable tools to develop deeper insight into existing subcultures, especially those that are marginalized in more mainstream and/or public arenas.
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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 | 0.041 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".