A Perfect Storm: How the Online Environment, Social Norms and Law Shape Girls' Lives
Bibliographic record
Abstract
Considerable scholarly and policy discourse has centred on dichotomous risk/opportunity; utopic/dystopic descriptions and prescriptions around girl’s and young women’s online interactions. Too often uninformed by the voices of girls and young women themselves, these discourses have frequently led to overly simplistic understandings of girls’ and young women’s seamlessly integrated online/offline existences. These top-down perspectives have produced reactive punitive policy approaches that blame girls for their misfortunes and incent parents and other adults to deny them their privacy by monitoring and surveilling them. Grounded in the literature and international legal standards that mandate participation of children in the formulation of policy and programs affecting them, with special attention to the needs of the girl child, this paper gives voice to the situated knowledges of the Canadian girls (ages 15-17) and young women (ages 18-22) interviewed about their experiences with online social networking by The eGirls Project researchers. eGirls participants described a world in which architectures structured to maximize disclosure (and minimize privacy) code high counts of “friends” and “likes” as “popularity”. These architectural constraints combine with social norms and marketing practices that encourage emulation of mediatized representations of female beauty and sexuality as ways of competing for recognition (often, for heterosexual girls, from males). Together these produce a perfect storm incenting self-disclosure that simultaneously promises both celebrity and recognition, but also a gendered risk of shame and harassment that is complicated by the enduring consequences of unnecessarily permanent digital records. These interactions invite policy responses that take into account the difficulty of navigating this complex environment and recognize the ways in which over-reliance on privacy-invasive surveillance based mechanisms undermines girls’ capacities to thrive in our increasingly digitally networked society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".