“Oh, She’s a Tumblr Feminist”: Exploring the Platform Vernacular of Girls’ Social Media Feminisms
Bibliographic record
Abstract
As avid social media users, it is perhaps unsurprising that feminist teenage girls use their favorite platforms to engage in various forms of feminist activism. Yet, existing research has not explored how a growing number of social media platforms and their technological affordances uniquely shape how girls engage in online activism. I address this oversight by asking the following: Why are girls using particular platforms for feminist activism? How do certain platforms facilitate distinctive opportunities for youth engagement with feminist politics? and How might this shape the types of feminist issues and politics both made possible and foreclosed by some social media platforms? To answer these questions, I draw on ethnographic data gathered from a group of American, Canadian, and British teenage girls involved in various forms of online feminist activism on Twitter, Facebook, and Tumblr. These data were collected as part of two UK-based team research projects. Using the concept of “platform vernacular,” I analyze how these girls do feminism across these different platforms, based on discursive textual analysis of their social media postings and interview reflections. I argue that teenage girls strategically choose how to engage with feminist politics online, carefully weighing issues like privacy, community, and peer support as determining factors in which platform they choose to engage. These decisions are often related to distinctive platform vernaculars, in which the girls have a keen understanding. Nonetheless, these strategic choices shape the kinds of feminisms we see across various social media platforms, a result that necessitates some attention and critical reflection from social media scholars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".