Negotiating With Gender Stereotypes on Social Networking Sites
Bibliographic record
Abstract
Research indicates that stereotypical representations of girls as sexualized objects seeking male attention are commonly found in social networking sites. This article presents the results of a qualitative study that examined how young women “read” these stereotypes. Our participants understood Social networking sites (SNS) as a commoditized environment in which stereotypical kinds of self-exposure by girls are markers of social success and popularity. As such, these images are “socially facilitative” for young women. However, the gendered risks of judgment according to familiar stereotypical norms are heightened by the intense surveillance enabled by SNS. While our participants indicated that a mediatized celebrity culture inculcates girls with messages that they must be attractive, have a boyfriend, and be part of the party scene, girls are much more likely than boys to be harshly judged for emphasizing these elements in their online profiles. Girls are also open to harsh criticism for their degree of publicness. The risk of being called a “slut” for having an open profile, too many friends, or posting too much information suggests that continuing discriminatory standards around public participation may effectively police girls’ capacity to fully participate online and complicate their ability to participate in defiant gender performances.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".