Soft Femme Theory: Femme Internet Aesthetics and the Politics of “Softness”
Bibliographic record
Abstract
Twenty years ago, the figure of the femme appeared on a book cover wielding a knife; more recently, the femme has appeared on Instagram crying in a Polly Pocket skirt. Online, femme is increasingly called “tender,” “radically vulnerable,” and “soft.” In this article, I examine soft femme digital culture on Instagram, and argue that the discourse of “softness” is used to articulate belonging and to resist neoliberal, masculinist logics. Through an analysis of Instagram images and interviews with femmes, I develop an understanding of softness as a combination of emotionality, vulnerability, relationality, and hyperfemininity. I argue that soft femme is an aesthetic form and a theoretical position that expands the category of femme to be more inclusive of diverse subjects. Soft femme theory fills the gaps in femme theory left by critiques of existing femme identity theory that centers performance, irony, and the figure of the hard femme. Through the discourse of softness, femme becomes something one can be like, rather than something one just looks like.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".