Bibliographic record
Abstract
The idea of devoting a special issue of Girlhood Studies to what Jonathan Bock (2012) calls technologies of nonviolence comes at a critical time in girlhood studies.On the one hand, technology-especially digital technology-and various social media platforms are firmly entrenched in the everyday lives of many young people around the world.On the other, questions regarding who has access to technology and how technology is used and abused continue to dominate the fields of girlhood studies in particular and of youth studies more broadly.Nowhere is this more evident than in relation to violence and social media in the lives of girls and young women.The many movements and organizations-some of them, like Take Back the Tech, 1 global in scope-are playing a key role in contributing to altering the landscape.The notion of technologies of nonviolence offers a frame, as it were, in which to set the various approaches, interventions, movements, and personal experiences that see technology as something that can be central to creating a counter discourse rather than one that is only about threat and danger.While this idea has been explored in the many versions of a topic that may be defined as "Social Media: Friend or Foe?" over the years, it is clearly one that is not going to disappear.The questions raised in this special issue explore this framing in a variety of ways that range from offering close readings of the vlogs of girls and young women in relation to rape culture through to a careful consideration of the legal implications of online violence, and from seeing the emancipatory potential in areas such as disability studies to the promotion of STEM (science, technology, engineering, and mathematics) for girls.At the same time, as several authors explore, there is still a great deal of work to be done, particularly in the context of sex trafficking but even (or especially) in approaches that explicitly seek to be non-
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.076 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".